Improved utilisation of the results from automatic surface inspection systems (IRSIS)
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European Commission
Research Fund for Coal and Steel Improved utilisation of the results from automatic surface inspection systems (IRSIS) Jens Brandenburger VDEh-Betriebsforschungsinstitut GmbH (BFI) Sohnstraße 65, 40237 Düsseldorf, GERMANY
Mathias Stolzenberg Salzgitter Mannesmann Forschung GmbH (SZMF) Eisenhüttenstraße 99, 38223 Salzgitter, GERMANY
Floriano Ferro ILVA S.p.A. (ILVA) Novi Ligure works, 15067 Novi Ligure AL, ITALY
Jose Diaz Alvarez ARCELORMITTAL ASTURIAS S.A. (AME) Centro Desarrollo Technologico, Apartado de Correos 90, 33480 Avilés, Asturias, SPAIN
Giuseppe Pratolongo Duferco La Louvière S.A. (Duferco) Hot Strip Mill Dept and Quality Control Dept, Rue des Ricaux 2, 7100 La Louvière, BELGIUM
Roberto Piancaldini Centro Sviluppo Materiali S.p.A. (CSM) Via di Castel Romano 100, 00128 Roma RM, ITALY
Contract No RFSR-CT-2006-00036 1 July 2006 to 30 June 2009
Final report
Directorate-General for Research
2012
EUR 25070 EN
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Table of contents
1 2 2.1 2.2 2.3 2.3.1 2.3.2 2.3.3 2.3.4 2.3.5 2.4 2.5 2.5.1 3 3.1 3.2 4 5 5.1 5.2
Final summary Scientific and technical description of the results Objectives of the project Comparison of initially planned activities and work accomplished Description of activities and discussion WP1: Preparation and assessment of current situation WP2: Preparation of data for generalized ASIS result utilisation WP3: Development of data utilisation procedures WP4: Integration and evaluation of developed procedures WP5: Assessment Conclusions Exploitation and impact of the research results Publications of gathered knowledge. List of figures and tables List of figures List of tables List of References Appendices Defect catalogue Cost benefit calculation of developed solutions
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5 13 13 13 14 14 23 43 86 108 114 115 116 117 117 120 121 123 123 128
1
Final summary
Task 1.1 Investigation of existing data archives and data formats from different installed ASIS The first semester of the project was embossed by preparing operations. First of all, the detailed studies of the current situation at the different plants were carried out. The necessary preparations for the project were performed and the current situation at the plants was assessed. The different inspection systems were reviewed and the most appropriate for the work to be done during the project were selected. Summarizing a total of 11 ASIS were involved in this project. 8 Parsytec matrix camera systems and 3 Siemens VAI line scan systems. All systems use standard database technology to store their results. The main information about features and performances of the installed ASIS systems have been collected and summarized in a detailed survey.
Task 1.2 Review of the current data usage situation Because there is no standard ASIS usage concept, every user utilizes the data in its own way. Therefore the aim of this task was to generate a survey on the daily work with inspection systems at all partners documenting the starting point of this project. Summarizing it can be stated that at the beginning of the project the ASIS installed at the partners were mainly used to: Support manual online inspection. Support manual coil grading/allocation procedures (Figure 2.3-3) Support manual protection of downstream facilities in case of heavy defects. Support manual cause-and-effect analysis for very bad coils. Use the archived data to meet customer complaints by finding out whether a special type of defect was present on the strip or not. All these utilisations of ASIS data were done complete manually. DUFERCO and ARCELORMITTAL ESPANA are using semi-automatic grading procedures based on rules. These rules allow thresholds on the number of defects which meet certain criteria like position and size. The outputs of these rules are supporting the manual quality decision. One main drawback of the manual approach is that the inspector‟s decisions regarding suspension of coils tend to be prudent. Thus a not negligible percentage of coils reviewed by quality engineers are in fact of good quality. This high percentage of “false alarms” implies high costs due to the wasted time and the consumed plant resources at finishing lines. Task 1.3 Investigation of quality and process relevant defect classes The investigation of quality and process relevant defect classes was done with main contributions of surface inspection teams of the production departments responsible for the training and operation of the inspection systems at the partners. A common layout for the defect catalogue was developed and an in depth study of the relevant defect classes was performed for all line types involved in the project. The catalogue contains as well a collection of conditions for the occurrence of severe defects as a selection of process variables influencing the surface quality to permit later correlation analysis. Because the main focus of this project lies in the utilisation of ASIS data the catalogue, organized in excel document, describes also the detectability of the main classes by ASIS. The resulting catalogue is included in Annex 5.1. It can be concluded that not all surface deficiencies can be recorded by ASIS. On each line type quality problems occur with a poor detection/classification capability of the state of the art systems. Therefore the existing systems can never be a replacement of the manual inspector, but a support. At ILVA main strip defects have been analysed by SEM equipped with EDS (energy dispersive spectroscopy) probe. For the most significant defect classes, images (and chemical analysis) coming
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from SEM have been co-related with images coming from Parsytec systems: it comes out clearly that some defects (e.g. inclusions, oxide and dark scratches) which have similar aspect in the Parsytec view, could be undoubtedly placed only using deeper analysis, such as those allowed by the SEM. Nevertheless the systems are able to identify the majority of surface defects sufficiently good and create an objective overview of production quality useful for further investigation. Of course all investigations done within this project were dealing with these classes. Task 1.4 Investigation of current product tracking capabilities The investigation of current product tracking capabilities was made at ILVA and ARCELORMITTAL ESPANA (AME) and led to completely different results. At beginning of the project, the product tracking capabilities from cold rolling mill to other process lines at ILVA were very low, whereas at AME the required information for a gapless material tracking was already available. To cover all aspects of material tracking anyhow it was decided to cooperate with ARCELORMITTAL EISENHÜTTENSTADT (AMEH) because the mandatory product tracking capability was also already implemented at this location. The partners determined that for the tracking of defect correlation over production stages two different strategies have to be distinguished. Single defects: For defects that appear as single event only (like heavy shells, holes, etc) a reasonable searching area to track the defects has to be defined. This searching area depends on the production steps and product tracking capabilities between the two inspection systems involved. After definition of a searching area a verification stage is needed to find proper correlation candidates within. This investigation was performed from AMEH hot strip mill to galvanizing line for shell defects. Area defects: For defects that appear in larger groups (like scale, dross, etc) the strategy of single defect tracking is improper, because of high computational effort and too many correlation candidates within each searching area. Therefore the approach of comparing defect distributions by continuous quality measures was selected (refer to 2.3.2.3 and 2.3.3.5 for further details). This investigation was performed from AME hot strip mill to pickling line for scale defects. Task 1.5 Tool specification and selection As result of investigation of the current situation at the partners it can be concluded that the improved data utilisation needs a two step approach. The first step regards the collection and preparation of data and is strongly related to the data situation at the plant and the designated usage application. Therefore different tools were developed for this step tailored to the specific demands at the partners. Reasonably a pre-processing of the ASIS data should be applied within this first step to prepare the mass data of the systems for the subsequent utilisation step. BFI developed a vendor independent postprocessing framework to allow at least a common approach for the handling of ASIS data. The second step deals with the application of data utilisation to define the main correlations between defect and process variables or applies advanced coil allocation procedures. For the data-mining application standard and commercial tools can be applied whereas for the advanced coil allocation two different approaches (Artificial Neural Network and Decision Tree) were selected for further investigation. Additionally it was decided to choose different technologies for the presentation of the results to the user. ARCELORMITTAL ESPANA and SZMF evaluated web-based tools allowing accessing the data with standard browser technology whereas the other partners decided for locally installed software. Because both approaches have drawbacks and assets this decision on the technology is more a question of the existing IT infrastructure and philosophy and will not be discussed further.
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Task 2.1 Integration of data structures The inspection data extracted from the ASIS databases consist of mainly geometrical data and data like classified type and severity, any ASIS is capable to deliver. So many different systems also from different suppliers can be collected in one database. This general approach of collecting the relevant data in one database was followed by all partners. According to the results of WP1 and the different data usage applications of the partners, various methods of data integration were applied. On the basis of the defect catalogue DLL has worked on the definition and integration of new important process variables not yet acquired. The set of these new variables is called “check-list” and it is defined as the collection of process variables and their description (range of acceptance, working status, time from last maintenance, and so on) that are related to the defect presence. The data integration for the vendor independent post-processing framework required the definition of an abstraction layer providing a mapping of common ASIS coil and defect attributes (position, size, class and grade) to the database structure of the specific vendor. Then the Post-Processing rules have to work on these abstract data structures, independent of the underlying system. Task 2.2 Acquisition campaigns of related process, customer/order and defect data Aim of this task was the generation of a data basis that was as well usable for method development as for method evaluation. The data collected should give a reliable and thorough production overview, so its collection was quite time consuming and proceeded during project duration. Because of problems with inaccurate ASIS data due to missing ASIS monitoring solutions the effort for data gathering and validation was significantly increased compared to preliminary planning. At SALZGITTER during the project period the data of more than 31000 coils have been collected in the inspection data base. The data were used for development and testing of post processing algorithms. Due to the amount of data and their reliability, the inspection results only from HDG2 in Salzgitter were used for post-processing. At ILVA three main DBs were collected, each referring to a specific processing line: all the possible ILVA Novi Ligure process routes have been considered (SPL, CAPL and HDGL), by means of Parsytec data, process data and quality/client/order data. Because there was no automation for DB extraction available, the data acquisition was a manual and slow operation. DLL and CSM prepared two main test databases in order to investigate the correlation of process parameters and quality problems. The first one was from Sept 2007- to Jan 2008 while the second one was from May 2009 to June 2009. BFI gathered data from AMEH hot strip mill (HSM) and galvanizing line (GL) for the investigation of defect evolution across the two production steps. The database contained data of more than 30.000 coils with 40.000.000 defects of the two inspection systems. Because the ASIS data available in the central database was already filtered (only main classes, no unclassified detections) it was decided to gather the raw data from the system databases manually. All data came from production of the first semester 2008. At ARCELORMITTAL ESPANA (AME) only coils were used that were not cut, thus a 1:1 relationship between HSM and PL result should be obtained. This particular task was very time consuming as no general gathering process was produced, but specific selections were done in order to avoid divided coils, data holes on process variables and many other specific criteria. Task 2.3 Definition of quality measures As basis for the usage of ASIS data, quality measures have to be defined to come from ASIS information to a quality description of the coil. Depending on the application of the data this quality description can be as well one scalar value as a feature vector usable for supervised learning strategies. As the most obvious quality measure the “number of defects of a certain class” was investigated at ILVA to find out, how far this feature would be sufficient for coil allocation applications. It could be shown that this variable appeared not to be enough significant to justify the decision made by the quality staff about coil grading.
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Due to that, other variables have been considered, such as defects maximum, position or mean area, and also some information given by the human line operator (i.e. the coil visual inspector). For the correlation of HSM and PL inspections and the description of scale affection a quite straight forward approach was selected:
track scale defects arising from HSM, estimate area and relevance
estimate the amount of scale removed in PL
"measure" cleaning capability of PL for some operational conditions by correlation of the results.
For having a common approach a defect is modeled like a Gaussian function, showing the defect‟s influence area. A large dataset of defects is considered by adding their Gaussian functions to a global defect map of the strip. Comparisons between facilities can be made by comparing these global defect maps. With this approach it is also possible to produce and measure iso-surfaces according to defect intensity conditions. Task 2.4 Implementation of task supporting tools According to the working plan the following tools were implemented within this project:
A vendor independent post-processing framework for defect filtering, verification and aggregation. (BFI)
A coil-map comparison tool for the investigation of area defect propagation (scale). This tool is web-based and implements the upper mentioned Gaussian quality measure. Additionally it is able to compare coils by means of their iso-surfaces (AME)
A coil-map comparison tool for the investigation of single defect propagation (shells). This tool uses the post-processing algorithms defined above to compare single defects. To investigate a huge amount of data for the implementation of quality rules this tool also provides an automatic search mode. (BFI)
Furthermore tools for the data gathering (CSM) and advanced coil allocation adapted to the specific plant situation (ILVA, SZMF) were developed. For the online integration of the cleaning capability model for coils at the pickling line developed by AME, additional tools for the visualisation of the results were also implemented. Task 3.1 Use post-processing to improve data reliability Most applications of improved ASIS data utilisation investigated within this project require an adequate post-processing to extract the desired information out of ASIS mass data. Task 3.1 investigated possible ways to improve data reliability by means of post-processing. Therefore two different types of rules were introduced and implemented in the post-processing software. Single rules: Using a condition part and a script part. As variables any defect and coil attribute stored in the respective database tables can be used. For each coil and each defect all conditions get evaluated. If the result of one condition is true, the script is executed for the current defect. As well changing defect attributes as creating new defects is possible, considering the correct allocation of new unique defect IDs. Context rules: Using the same procedures as single rules this type allows also the definition of a context rectangle around every defect. In the script part not only the defect attributes itself, but also all defects within the defined region can be accessed and thus rules like “more than 10 scale defects in the neighbourhood, than also scale” can be realized.Some defect classes with overlapping classification could be identified and reclassified using these context rules.
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Task 3.2 Use post-processing to condense defect data One by far more important motivation for the application of post-processing is the aggregation of single detections to cumulated defects because the amount of defects that have to be considered in subsequent utilisation steps decreases significantly. A method has to be found to indicate clearly severe defects, show defect aggregations and their geometrical relationship without loosing too much information and to keep information about the background of single defects or indications not integrated in larger defect collections. In any case a significant reduction of the number of defects and a clearly structured coil map is the aim that has to be reached. For the post processing of inspection data at SZMF software working automatically on the collected data was developed, that can group defect agglomerations of different shape to defect areas. Data of the defect areas are stored in separate tables in the database. All relevant details like defect density, average values, standard deviation and minimum / maximum values of defect areas, distances, length and width are stored together with their geometrical data This kind of post processing enabled recognition of patterns across the strip being used for coil diagnosis without sticking too much to individual misclassifications of single defects. Also severe defects are indicated clearly and evaluated individually. The condensing of defects in defect maps is performed by software developed in Salzgitter. A decrease of indications on the coil map of a factor of 20 to 30 can be reached without loosing relevant information and keeping of the most important statistical data. Task 3.3 Development of best practice data preparation approaches For the process data correlation the preliminary work on the single variables (to understand their influence in the process and to transfer the process know-how) has consumed long time and moreover the first correlation tests have been insufficient. It has to be stressed, that this delay was mainly caused by problems with inaccurate ASIS data due to missing ASIS monitoring solutions, increasing the effort for data gathering and validation. Due to lack of methods for automatic ASIS data assessment the data evaluation is very time consuming and ASIS data verification has to be done manually to ensure mandatory data reliability. Several coils previously collected were not usable because of uncertain results and more data was required for improved method development. Therefore much effort was put into data preparation to produce reliable data for further investigation out of the existing archives. The gained knowledge was summarized in a schematic best-practise data preparation guideline. Task 3.4 Process data correlation and emerging conditions of defects At DUFERCO the analysis activity was focused on the correlation between defect and process. Investigation was done on the continuous casting process, roughing mill and finishing mill process. The relative investigation was done with different samples of data depending on their availability. An important test was also done on the comparison of ASIS and operator classification because this was one main critical aspect of project. At SALZGITTER for the prediction of appearance of stickers at the skin pass line a classification model was developed. With a support vector machine stickers can be predicted from the process values up to 89%. With this model the most significant influences from the process conditions were analysed and several important parameters identified. Some of them, but not all were already known At ILVA the HDGL database has been examined to find correlations among three different data subsets: Parsytec, process and quality/client/order data have been considered. During the course of the project the available process data have been quite poor, therefore the correlation analysis has been limited to few process parameters. Nevertheless, a case study regarding the so called Nozzle stripe defect has been presented. Some well-known correlation between process parameters and defects could be con-
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firmed by ASIS results. Some results are robust and clear, some others are not confirmed by experience of ILVA technical staff. At ARCELORMITTAL ESPANA the measures defined in task 2.3 where used to define a supervised learning system for cleaning at the PL, in which defects are represented by surface areas that have defect values that exceed a certain threshold. The output of the resulting model was investigated regarding the sensitivity for the change of some process variables given as input. This additional capability is being used, from a technical point of view to fix acceptable conditions at the PL; and strive to find a balance between line capacity and line efficiency. Task 3.5 Different algorithms for defect mappings Within this task defect mappings were developed in order to examine the defect evolution over production stages. Different strategies of the tracking of defect correlations were followed. Depending on defect characteristics two main approaches were identified by the partners and investigated separately: Single defect mapping (like heavy shells, holes, etc.): To gain general knowledge about evolution of defects across production steps and generate quality rules a huge amount of coils has to be evaluated. Therefore BFI developed an automatic correlation analysis that can process thousands of coils with millions of defects to generate quality rules. To realize this automatic searching BFI integrated the context search used within the post-processing algorithms developed in task 3.2 into the defect mapping solution. Large sets of coils can be iterated automatically and associated defects can be collected. Afterwards the most relevant associations were presented and stored in a database for further investigations. Area defect mapping (like scale, dross, etc) With the definition of a quality measure based on Gaussian functions AME has introduced a method for considering all types of defects at a glance in which there is an interest, instead of considering each defect in an isolated fashion. Therefore the next step in seeking quality control improvement was related to a comparison of the defect maps from each facility involved in the transformation of the steel strip. Because of missing performance monitoring solutions for ASIS, AME proposes this quality measure as automatic reliability check for the available data. Therefore the defect maps of HSM and PL were compared regarding their scale affection and a simple performance monitoring rule was introduced. Assuming that the PL does not create scale defects the systems have to be maintained if they produce inconsistent scale maps. Task 3.6 Improve coil grading At ILVA many investigations have been done on the gathered datasets: SPL and CAPL DB analysis lead to the design of a neural network based algorithm for automated coil allocation based on Parsytec data. Data pre-processing revealed to be the hardest task in order to achieve good results. A decision system for automated coil classification has been designed as the integration of two neural network decision makers, before (pre-decision) and after (post-decision) the in-line inspector (HLO). Using of this tool could lead to a strong decreasing of the quality department load, theoretically from 8.7 % of CAPL DB to 0.7 %. The main problem pointed out from the results analysis was the excessive amount of misallocated coils at the pre-decision subsystem allocating defective coils as good, evidently not allowed in a real industrial application. At SALZGITTER for improved coil grading features describing the quality state of a coil were derived from the condensed defect maps. From the condensation algorithms six main types of defects were generated that were used to generate feature sets for later coil allocation. At the final dataset only one row of data is left for each side of the coil. Different feature sets were examined to improve coil grading. It was shown that an approach using direct information coming from the post-processor (like number of defects) will perform better than a threshold based approach applying labels for different defect loads before classification.
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Task 4.1 Advanced coil allocation At SALZGITTER for advanced coil allocation several classification algorithms like neural networks or support vector machines were trained and tested. Finally again a decision tree was found to be the most appropriate way to predict the coil release. Main problem was a high degree of false decisions where a coil is released of the prediction system that had to be stopped of about 5%. This could be diminished below 1% but only on charge of the overall performance. At ILVA the preparation and tuning of a semi-automated decision system based on ANN at SPL and CAPL was investigated. The PrePost decision approach performance appears to be encouraging but at now not industrially acceptable. Taking into account the relative occurrence of events related to human and ASIS inspection in the plant, a feasible scheme of advanced data usage has been proposed, merging the experience gained in the frame of the project. Besides these coil allocation approaches at finishing lines the model of cleaning capability at AME was used to predict if a coil can be correctly pickled or not. Therefore it was also proposed to use the model output for advanced coil allocation already in HSM using this model with the only PL variable “pickling speed” considered. Choosing a high value, close to the maximum line speed this model can be used to predict if the coil is supposed to be correctly pickled under normal pickling conditions, even at a high speed, increasing line throughput. Choosing a low value for the pickling speed, close to the speed used for problematic coils this model can be used to predict, already at the exit of the HSM, if it could be convenient to reduce the pickling speed to ensure the correct quality of the pickled coil, leading to less downgraded material in finishing lines increasing the yield.
Task 4.2 Control of downstream process By means of surface inspection results from HSM and knowledge of through process scale transformation, the process control of the pickling line could be optimized. Considering the entry state of the strip concerning the scale affection and the current state of the pickling line in the corresponding situation it can be decided whether the coil can be cleaned successfully with normal PL speed or if it can be pickled with reduced speed or if a successful removal of the scale from the coil surface will not be possible. AME developed some screens providing the cleaning model output information to the line operator who can decide about the optimal processing speed for the incoming coil. Some examples are presented where the results of the introduced model are more accurate than the ones of the existing grading rules. This is one example of the limitations of these rules (based only on the ASIS results: defect number, severity and distribution) and how the new model can help to provide a better help to the inspectors. Task 4.3 Optimisation of upstream process The model introduced at AME additionally was used for the optimization of the hot strip mill process. Therefore a screen was prepared allowing the quality experts to see what the model had predicted and the main reasons for this prediction. If the model results are correct, this can be a great help in order to discover which process parameter need to be changed to try to avoid similar problems in the future and improve the final product quality. Furthermore BFI evaluated the developed tool for automatic defect tracking as improved software solution for cause-and-effect analysis of surface defects. Therefore an investigation on the tracing of correlated shell defects from HSM to GL at AMEH was performed and the presented approach of finding relevant rules for the propagation of defects can be seen as best-practice procedure easily transferable to other surface quality problems. Furthermore two studies of CSM and DUFERCO regarding the results of cause and effect analysis at DUFERCO HSM are presented explaining the possibility to use the developed procedures for cause-and
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effect analysis for the process optimisation of the upstream processes ensuring safe plant operation based on surface defect data. Task 5.1 Test of functionality and system tuning A significant number of tests and inline implementations have been carried out, considering difficulties due to the economic situation. It was possible to verify the relevance of tools developed, even when additional developments are required for different steel grades and more integration is required providing tools usable by operators. The test of the coil allocation with the decision tree at SALZGITTER was performed at data from actual production, about 1600 coil sides. An operator decision for release or stopping was collected from the manual recordings. The result was the same as before. Again the number of critical decisions could be held very small but at charge of the overall accuracy. Task 5.2 Determination of cost/profit relation For a comparable assessment as well for the cost/profit relation as for the transferability of the developed solutions a survey was invented. This survey was send to each partner and filled for any developed solution. For a detailed overview on each single solution please refer to the Annex 5.2 It must be stated that most of the profit calculation can just be tentative, since the actual assessment depends on many unknown parameters, which are not foreseeable at now.
Task 5.3 Determination of transferability The software developed and the database structure uses only data that will be delivered from any modern surface inspection, mainly classified defect type, geometrical defect data, inspection sensitivity and coil information being available also for the operator. Due to this structure the system can be fitted easily to other production lines. By far the most important precondition is a system with a high detection and classification rate. For detailed information about the transferability of all developed solutions please refer to Annex 5.2.
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2 2.1
Scientific and technical description of the results Objectives of the project
The overall aim of the present project is to improve the final surface quality of flat steel products, to reduce production costs and to increase the yield. To aim this ambitious target the utilisation of the results of automatic surface inspection systems will be improved by development of proper methods and techniques and if necessary their realisation as software tools. Therefore the project focused on the following points: General methods for the grading of all types of surface defects will be available and could lead to a European standard in this field. A tool for the comparison of coil maps will be developed. Based on the above tool the life cycle of defects will be investigated and the knowledge about surface defect nature, origin, emersion and possible avoidance will be increased. Robust methods for the data based cause-and-effect analysis of surface defects will be developed and based on this results production rules will be generated to optimise the upstream process. Coil allocation procedures will be optimised based on the improved knowledge of the life cycle of defects and the generalized grading algorithms. Solutions for the control of downstream processes will be developed and tested at the example of scale reduction in the pickling line. 2.2
Comparison of initially planned activities and work accomplished
The main objectives of the project have been achieved. One major task of WP3 was the investigation on correlation between process status and defect presence (detected by ASIS). This investigation was more challenging than expected and timing delay was encountered within the project limits. For the process data correlation the preliminary work on the single variables (to understand their influence in the process and to transfer the process know-how) has consumed long time and moreover the first correlation tests have been insufficient. It has to be stressed, that this delay was mainly caused by problems with inaccurate ASIS data due to missing ASIS monitoring solutions, increasing the effort for data gathering and validation. The classification reliability at DUFERCO hot strip mill, concerning defects from casting process (shell), was too low. When the ASIS classification was used as operator assistance the classifier was considered satisfactory, but in comparison with the application of statistical and mathematical model in correlation analysis, the classifier was not adequate. For this reason a different investigation approach was taken and some ASIS classification adjustment was done. At least some satisfactory process correlation results have been achieved for the shell and scale classes, the main critical defects at hot rolling stage, on the basis of quality staff experiences and confirmed by the experimental collected ASIS-data. Concerning the development of a diagnosis system at HSM for the optimisation of upstream process the activity not matches completely with the planned one because more time was spent for previous WP3 and the set of rules of diagnosis system is not implemented as automatic software procedure, but is organized as a list of special warnings to be followed by quality operator during the coil grading and coil allocation procedures. However some important rules were transmitted to upstream process for defect removing. Furthermore because of the financial crisis and operation downtimes during 2009 the activities for the final assessment and validation of the project results (WP5) were suffering some delays.
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2.3
Description of activities and discussion
2.3.1
WP1: Preparation and assessment of current situation
Because there is no standard in ASIS data usage every partner deals with the data in his own way. This work package has led to an exhaustive overview about the current situation, necessary for later method development and implementation. BFI developed a common form to collect the main characteristics of the different ASIS involved in this project. As well data archive as current data usage information was included in this form to mark the starting point of this project. The form was sent to each partner and filled out by the people dealing with the single systems.
2.3.1.1
Task 1.1: Investigation of existing data archives and data formats from different installed ASIS Partner
Systems
SZMF
3
ILVA
4
ARCELOR ESPANA 2 DUFERCO 2
Lines EGL, 2 x HDGL Electro Galvanizing Line, Hot Dip Galvanizing Line PL, CAPL, SPL, HDGL Pickling Line, Continuous Annealing Process Line, Skin Pass Line HSM, PL Hot Strip Mill HSM, PL
vendor
SIEMENS VAI
PARSYTEC PARSYTEC PARSYTEC
Table 2.3-1: Systems involved in this project As shown in Table 2.3-1 a total of 11 ASIS are involved in this project. 8 Parsytec matrix camera systems and 3 Siemens VAI line scan systems. All systems use standard database technology to store their results. For a detailed description regarding the data formats used by the single systems please refer to the midterm report. All inspection systems provide a main table containing general information about the inspected coils and a detail table with all detected defects, their size, position and class assigned by the automatic classification. The results are usually visualised as a so called coilmap showing all detected defects according to the position on the coil in a 2-dimensional plot. The start point for any further analysis independent from the system manufacturer is therefore the content of the database and the defect map
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SZMF EGL SZMF HDGL 1 SZMF HDGL 2
Detection average detection rate of most relevant classes (%) 70 70 70
average pseudo detection rate (%) 15 5 10
Classification average average defect load classification per coil rate (%) 1000 70 1000 70 1000 70
best classification rate (%) 90 95 95
ILVA PL ILVA CAPL ILVA SPL ILVA HDGL
75 85 85 90
30 10 10 10
10000 6000 20000 3000
75 85 85 90
80 90 90 90
85
58,5
1950
78
87
85
60
5500
60
83
DUFERCO HSM DUFERCO PL
80 80
20 20
600 2500
55 60
70 95
average Minimum Maximum
79,55 70 90
22,59 5 60
4777,27 600 20000
71,64 45 90
87,36 66 95
ARCELOR NA HSM ARCELOR NA PL
ESPAESPA-
Table 2.3-2: Summary of ASIS states Besides the data structures used, the upper survey investigated the reliability of the existing data archives. Table 2.3-2 summarizes the result in terms of detection and classification performances. Because there is no standard in ASIS data assessment the results are only conditional comparable. This is obvious the case for the results of the column “pseudo detection rate”, because every partner defines the detections, which should be declared as pseudo, on his own way. (e.g. at ARCELOR ”dirt”, “water” and some other classes are pseudo classes whereas at DUFERCO, ILVA or SZMF they aren‟t) Because the standardized assessment of ASIS results was not an objective of this project this problem could not be solved. It has to be stressed, that the lack of standardization in this field increased the effort in data gathering and data validation significantly. At hot strip mill (HSM) plant of DUVERCO La Louviere (DLL) the global classification performance was < 50% with maximum rate , equal to 66% for roll marks class defect and worse case of 14% for dirt class defect. Other important classification aspect was the confusion between water (not important class) and salt and pepper scale (very important class). These performances, compared with the good average detection > 80% and the list of main classes, were been improved by DLL with a preliminary new tuning activity in order to assure a minimum average classification rate for the main defect classes. The average classification defect was tuned to 90% to the training set composed with more than 200 sample defect images divided into 15 classes. The same test of self classification with the old one status was the 76%.
15
Figure 2.3-1 Class distribution at SALZGITTER HDG 1
Figure 2.3-2 Distribution of defect width (top) and length (bottom) The available data archives at SALZGITTER were analyzed statistically by BFI and showed a high amount of class “dross” and “unknown” (Figure 2.3-1).
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Class “unknown” is applied when the confidence level of the classification is not sufficient high. Class “dross” mainly consists of very small defects and even the distribution of the defect dimensions (Figure 2.3-2) shows clearly a high amount of very small defects (width = 1-2mm, length = 4mm). One conclusion that was taken from these investigations was the clear demand of data post-processing before further usage. The huge amount of small defects has to be aggregated to a reasonable amount of data without loss of information as one major task of WP3. It can be stated, that a comparable investigation on class distributions is mandatory for any development of data usage applications. Process data At DUFERCO La Louviere (DLL) plant the hot area is missing, as the slab were supplied by others plants. For this reason all process variables from continuous casting are not directly available for correlation analysis. This problem could be solved by achieving an agreement with the most important slab supplier CARSID in order to obtain the continuous casting data. All data are now delivered with the slab in text file format. Although all data sources are accessible from network plant, the data integration was not organized and it was necessary to develop new software tools in order to ease the automatic gathering of quality and process data. 2.3.1.2
Task 1.2: Review of the current data usage situation
Because presently there is no standard ASIS usage concept, every user utilizes the data in its own way. Therefore in the following a short summary of the common approaches will be explained. At the beginning of the project there were the following main usages of ASIS: Support manual online inspection. Support manual coil grading/allocation procedures (Figure 2.3-3) Support manual protection of downstream facilities in case of heavy defects. Support manual cause-and-effect analysis for very bad coils. Use the archived data to meet customer complaints by finding out whether a special type of defect was present on the strip or not. All these utilisations of ASIS data are done complete manually. Figure 2.3-3 displays the general coil allocation procedure, which summarizes the approaches of all partners in one schema. Starting with all coils of a production the inspector checks online for defects supported by the surface inspection system. Coils with no defects pass and are send to the downstream process and the customer respectively. The worst coils of the shift report go to the next level and are further investigated offline by looking at the results of the surface inspection system. Here the second decision is made if the coil is send to the customer or suspended. Suspended coils come to the quality engineer who has to decide if the coil is ok for the customer ok for another customer or if it has to be downgraded, scraped, or should be repaired at finishing lines. Sometimes this is also a two level approach and level 2 and 3 are both done by the quality engineer. The task for ASIS data usage is now to support this completely manual process by means of automatic coil allocation procedures providing coil allocation suggestions to the quality engineer who can either use or refuse them. DUFERCO and ARCELORMITTAL ESPANA are using semi-automatic grading procedures based on rules. These rules allow thresholds on the number of defects which meet certain criteria like position and size. The outputs of these rules are supporting the manual quality decision (level 2).
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One main drawback of the manual approach is that the inspector‟s decisions regarding suspension of coils tend to be prudent. Thus a not negligible percentage of coils reviewed by quality engineers is in fact of good quality. This high percentage of “false alarms” implies high costs due to the wasted time and the consumed plant resources at finishing lines.
Figure 2.3-3 Schematic representation of coil allocation procedure
2.3.1.3
Task 1.3: Investigation of quality and process relevant defect classes
The investigation of quality and process relevant defect classes was done with main contributions of surface inspection teams of the production departments responsible for the training and operation of the inspection systems at the partners. A common layout for the defect catalogue was developed and an in depth study of the relevant defect classes was performed for all line types involved in the project. The catalogue is a collection of conditions and process variables influencing the occurrence of surface defects. The catalogue, organized in excel document, describes the main defects, their detectability by ASIS, their causes and effects for production. The resulting catalogue is included in Annex 5.1. A collection of conditions for the occurrence of severe defects as well as a selection of process variables influencing the surface quality was made to permit later correlation analysis. At ILVA main strip defects have been analysed by SEM equipped with EDS (energy dispersive spectroscopy) probe. For the most significant defect classes, images (and chemical analysis) coming from SEM have been co-related with images coming from Parsytec systems: it comes out clearly that some defects (e.g. inclusions, oxide and dark scratches) which have similar aspect in the Parsytec view, could be undoubtedly placed only using deeper analysis, such as those allowed by the SEM. Summarising the classes of defects influencing the product quality could be separated first by the production step where they were generated. Generally it is possible to ascribe a certain defect type to a specific production step, but there are also very similar defect types coming from different production steps.
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The metallurgical production step (Slab casting) produces defects like slivers, shells and similar defects on the surface. All of them have strong influence on the product quality. Metallurgical defects in many cases are discovered only after rolling the material to a certain extend. In this case a covering layer of steel becomes weak enough to be torn off from the surface. So they get visible just during the final production steps. The next production step where defects could be produced is hot rolling. The comparison of each production line (HSM for DLL and ARCELOR MITTAL) has led to the identification of 4 main defect classes as the SHELL, SCALE, SCRATCH and PERIODICAL. Each class is divided into other specialized classes: shell, shell edge, shell lengthen for SHELL, sagger, salt & pepper, peeling work roll for SCALE. These were defined as the more critical defects in comparison with PERIODICAL, caused by rolls, and SCRATCH ones. An additional defect class OTHER contains a lot of secondary class used for quality coil grading. Anyway the list before was commonly accepted as the base required from a general ASIS at HSM. The next defect producing production step is cold rolling, where mainly periodic defects and holes come from. At last also the finishing lines can produce defects that could exclude the material from further usage. The main defect class having its origin in the hot dip galvanising line is dross from the galvanising bath, influencing the surface quality to a large extend. Due to the large number of small particles left on the galvanised surface as a measure a surface density seemed to be appropriate. So the number of particles has to be count and related to the reference surface area. Another important defects are uncovered regions, influencing the strip quality to a high degree. For the electro galvanising line, main classes are defects originating from the electro galvanising process like anode contacts and uncovered regions. Additionally pseudo defects in shape of stripes and streaks due to the rolls within the production line are mainly being confused with severe defects like shells, slivers and laminations and are identified as important classes. 2.3.1.4
Task 1.4: Investigation of current product tracking capabilities
For the later examination about defect evolution over the production chain, it is mandatory to investigate the product tracking possibilities at the partners in detail. These investigations were made at ILVA and ARCELORMITTAL ESPANA and led to completely different results. At ILVA the current product tracking capabilities from cold rolling mill to other process lines (CAPL, HDGL and SPL) are very poor due to the following points: o
o
o o
o
The ASIS at PL detects defects on a strip which has to be cold rolled: the position of defects after cold rolling varies due to the thickness reduction which leads to length increase. Though the defects position shifting could be calculated from the achieved thickness reduction, no automatic system to do this operation is currently available. The level of instruction of PL ASIS is low, and the main defects detected at CAPL, HDGL and SPL, such as inclusions and oxides, are not well detected at the Pickling Line. These are also the main defects of ILVA production. After cold rolling, before coiling, it is possible that the head of the strip is cut by the operator because of the presence of serious shape or thickness defects. At the Continuous Annealing and Hot Dip Galvanizing lines, some cuts are made at the head and/or the tail of the coils, in order to eliminate parts which are out of thickness tolerance. The only available information is the weight of the coil after cold rolling and after the successive process line. No information is given about the exact amount of strip which is cut was available during the project activities period. In HDGL top and bottom surface may swap with respect to rolling mill, depending on the entry uncoiler in use.
Thus huge effort is needed to be able to track information lined up by coil instead of by facility as it was done so far, which cannot be part of this project. To be able to perform investigations of process data correlation (Task 3.4) anyway, it was decided, that ILVA should examine defects having “local” causes, especially at the HDGL.
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At ARCELOR ESPANA a software tool has been developed to perform product tracking. This approach will allow identifying trimming steps, coils‟ reversion and other operations. Therefore the required information is collected automatically from each facility and summarized graphically. Figure 2.3-4 shows a screenshot of the tracking of one coil from the basic oxygen furnace (with the tonnes of hot metal) to tandem mill (showing the length of the coil in each facility). With this tool it is also possible to obtain general characteristics of the product like width, weight, date, thickness and so on.
Figure 2.3-4 Tracking sample of a coil Strategy of tracking correlation of defects Besides the mandatory product tracking capability the partners determined that for the tracking of defect correlation over production stages two different strategies have to be distinguished. Single defects: For defects that appear as single event only (like heavy shells, holes, etc) a reasonable searching area to track the defects has to be defined. This searching area depends on the production steps and product tracking capabilities between the two inspection systems involved. After definition of a searching area a verification stage is needed to find proper correlation candidates within. Area defects: For defects that appear in larger groups (like scale, dross, etc) the strategy of single defect tracking is improper, because of high computational effort and too many correlation candidates within each searching area. Therefore the approach of comparing defect distributions by continuous quality measures was selected (refer to 2.3.2.3 and 2.3.3.5 for further details). For the investigation of defect life cycles besides the direct hot strip mill to pickling line connection, cooperation with ARCELORMITTAL EISENHÜTTENSTADT (AMEH) could be established. Within the RFCS-project RFS-CR-03041 “Factory-wide and quality related production monitoring by datawarehouse exploitation (FACTMON)” an exhaustive product tracking solution was implemented at AMEH motivating the decision for that cooperation. Herein automatic inspection data from hot strip mill and galvanizing line will be examined. Please refer to 2.3.3.5 for further details.
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2.3.1.5
Task 1.5: Tool specification and selection
Fitting the demands of the data usage application different approaches of the tools to be developed and used within this project were chosen by the partners. ARCELORMITTAL ESPANA and SZMF evaluated web-based tools allowing accessing the data with standard browser technology. One main advantage of this solution is that installation and maintenance procedures just appear for the server and not for any client using the system. These tools were already able to provide single coil information and defect maps and were further developed to provide results of the algorithms developed for coilmap comparison, grading, coil allocation and information aggregation. By using these tools different campaigns of data collection were performed in order to get enough samples allowing learning main dependencies or having a relevant number of cases for mining. Furthermore SZMF and BFI decided to use the DataSIS software developed by BFI for statistical evaluation of ASIS mass data and development of post-processing rules. Therefore the data interfaces were enhanced to allow vendor-independent analysis of ASIS data. As a flexible interpreter language usable for the execution of post-processing rules BFI evaluated MATLAB. It was integrated successfully in a .NET based framework and was chosen as basis for further development. However with increasing complexity of the rule-base used for post-processing it came out that this approach is by far too slow for productive usage. Therefore finally C# was chosen as rule definition language leading to about 30-times faster execution times and providing therefore sufficient execution speed even for more complex rule-sets (see 2.3.2.4). To pursue the aim of designing a tool for automatic coil grading, artificial neural network (ANN) has been the choice at ILVA. An artificial neural network, often just called "neural network”, is a mathematical model or computational model based on biological neural networks. It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation. In more practical terms neural networks are non-linear statistical data modelling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data. Some ANN features revealed to be decisive for the choice of using this algorithm for coil grading: o
Non linear relations modelling capability
o
Real data modelling without a priori knowledge
o
Robustness: ANN are slightly affected by bad or lacking data
o
High generalization capability: if training is good, ANN are able to give sensible predictions also with data “distant” from those used for training
Although at first it was thought to develop a multi-classifier (one classifier for each considered defect class, all combined in a final classifier), as in Figure 2.3-5, the first data analysis showed that finding an efficient classifier for each defect class would have been a too hard task: no strong correlations have been found in SPL and CAPL database between many specific defects and Quality Department decisions.
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Figure 2.3-5: First classifier tool proposal at ILVA, not applied because not adaptable to actually available data Therefore the focus has been shifted on a single classifier able to process all the selected variables (referred to various defects) at the same time. Operating with a unique classifier allows to exploit all the above mentioned ANN capabilities; for example, in the discontinued multi-classifier approach, the relation between the classifiers, which leads to the final decision, would be linear or threshold based. Considering the complexity of the coil grading task, this has been considered as strong limiting factor.
Figure 2.3-6: Schema of generic artificial neural network The tool should be able to operate the classification considering a relatively small set of information coming from the Parsytec system and subsequently pre-processed by the tool itself. Furthermore, the software tool could give some useful information about how to re-allocate the coil. For example, if the system finds all or most defects in a well defined region, it could point out the possibility of splitting or trimming the coil to preserve material from unwanted scrapping. Starting from the integration of all the data structures, the aim was to define exactly all data and quality measures useful to develop and to test an automatic coil grading software at the exit of the process line. As result of investigation of the current situation the project needs 2 main software tools are required for data utilisation applications. The first one regards the collection and preparation of data and is strongly related to the data situation at the plant, while the second one deals with the application of data utilisation to define the main correlations between defect and process status or provides advanced coil allocation procedures. For the data-mining application standard and commercial tools can be applied
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where for the first step custom software has to be developed. CSM has designed and developed new software tools for automatic gathering of quality and process data. Additionally SZMF has developed a program for automated collection of the relevant ASIS data from the original Sybase database. This program uses a JAVA algorithm, ODBC and JDBC drivers to connect with the ASIS database or the MySQL database respectively. The program was installed on a data server in the development department and is automatically activated at fixed time intervals by an automatic time scheduler. For the technical approach of data mining two different software solutions were selected by DLL and DUFERCO. The Pepito software was selected by DLL as daily activity tool of Quality Staff. The software has been used for the application of Dendogram description method, for the pruning of variable strongly correlated and the application of Decision Tree model for the study of defect-process correlation. The software presents an easy approach to data mining with user friendly interfaces that guide the operator to the application of simplified statistical models. The SAS (Statistical Analysis Software) software was selected by CSM as statistical framework for data mining, it is tailored for skilled operators, used for off-line laboratory analysis and it is purchasable only with annual rental licence. The software contains all the statistical methods and models with the possibility to develop procedures, with a custom program language, for manage a big huge of data. In this project SAS was used for the statistical data description, for the application of regression analysis and Logistic regression model for correlation analysis. As support tool of classification ASIS tuning, the software tool CBE (Classification Build Environment) by Parsytec was selected and used. The software guides the quality operators to quickly prepare a solid defect samples set for the tuning of classifier with a big time reduction. Besides the classifier performances checking is also performed by the same tools. Data mining methods for improved coil allocation were tested with two different software packages, “Rapid Miner” from University of Dortmund and “Knime” from University of Konstanz, both freely available. Using these software packages, models with decision trees, neural networks or other learning algorithms could be trained at condensed defect data labelled with real decisions of the production as “ok” or “not ok” for the specific coils. The trained models from the Rapid Miner can be included and run in a JAVA software, whereas the Knime models could be run only within the Knime software environment 2.3.2
WP2: Preparation of data for generalized ASIS result utilisation
The first step in data utilisation improvement was the consolidation of all application relevant data defined in WP1 to build up an exhaustive and meaningful data basis for further investigations in WP3. Dependant on the different usage applications the tasks followed different approaches. 2.3.2.1
Task 2.1: Integration of data structures
The inspection data extracted from the ASIS databases consist of mainly geometrical data and data like classified type and severity, any ASIS is capable to deliver. So many different systems also from different suppliers could be collected in one database. This is important for the setup of a data warehouse as shown in Figure 2.3-7
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pickling lines batch anealing hot strip mill Technical Data Warehouse
skin pass line elektrogalvanizing line HDG lines
casting mill Process data recording
organic coating line
SIS data defects, quality state Product properties
Analysis of - Cause and reaction chains - Process conditions …
Figure 2.3-7 Integration of the ASIS data in a future Data Warehouse This general approach of collecting the relevant data in one database is followed by all partners. According to the results of WP1 and the different data usage applications of the partners, various methods of data integration were applied. At SALZGITTER the focus lied on the automated collection of the relevant ASIS data. The data structure of all the surface inspection systems being part of the project was very similar except some small deviations. At both the HDG lines the steel grade information was missing. One task for data integration was therefore to provide the correct steel grade information from other data sources. Initially A program was developed to collect this information and store it to the MySQL database for further evaluation. On the other hand the steel grade is implicitly given by the inspection sensitivity that is passed to the inspection system from the material information system of the production line. The inspection sensitivity is directly related to the surface quality requirements and therefore to the tolerable defect level. So the inspection sensitivity showed up at the training of coil allocation models to be more significant for the surface quality than the individual steel grade. Figure 2.3-8 shows the integration of the data structures for the three galvanising lines. The green boxes show the existing data structure for the collection of the Data from HDG 2. In the same way the connection to the HDG1 and ELO was setup and tested. It is presently not in use either due to the not sufficient reliability of the results in case of the ELO or in case of the HDG1 due to the high similarity to the task at HDG2. Data from the databases of the inspection system are automatically collected and evaluated. The collection program was installed on a data server in the development department and is presently activated every 60 minutes. New data are identified by comparison of the recent timestamp with the latest timestamp in the database. All timestamps higher than the latest one are collected and transferred to the MySQL database. This collection scheme and the dedicated table structure were setup in a way, that it can be extended on all inspection systems of Salzgitter plant and would give a uniform data structure. Extensions of the tables with new columns and introduction of new tables for evaluated data can be added any time if necessary.
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Figure 2.3-8: Integration of data structures for the collection Inspection data from the galvanising lines. The ASIS application evaluates the inspection data by condensing and extraction of key figures (Green: Existing data structures; Red: Tested but not established structures)
Figure 2.3-9 Concept for application of the BFI tool for post processing
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Two main tables are filled during collection of inspection data, a summary table containing global data for the inspection and the specific coil and a detail table with all necessary information about the single defects. The evaluation and visualisation software is using these tables with a fixed internal structure and column specification. After evaluation the results were stored also in two different tables, one for detail information about condensed groups of defects and one containing a single table row for each side of a coil. This row contains key features of the defect distribution found on the specific coil side and can be used later on for classification for coil allocation. A more common approach was followed by BFI wanting to generalize this approach with the implementation of custom rule-sets that can be implemented in a parameter file without changing the program itself. Within this vendor-independent post-processing framework rules for filtering, aggregation and verification of the data can be added adapted to the specific needs of the data usage application (Figure 2.3-9). On major design aspect was the flexibility of the system. Because of the general concept it should be possible to adapt the ASIS processing step to the internal processes of the user without huge effort. To achieve vendor independence an abstraction layer had to be developed providing a mapping of common ASIS coil and defect attributes (position, size, class, grade) to the database structure of specific vendor. Another problem arisen within this project was that these values are not stored in common units. Whereas the first vendor stores the downweb position of a defect in meter the other vendor stores it in mm. Therefore different units stored in the ASIS databases had to be supported and also arbitrary data types (e.g. defect class as string or integer) and databases (oracle, mysql, sqlserver, access, etc.). Summarizing it can be stated that the Post-Processing rules have to work on abstract data structures, independent of the underlying system as shown in (Figure 2.3-10). Therefore in order to integrate any inspection data BFI developed a configuration tool that allows the free allocation of common surface defect and coil attributes to arbitrary database columns and length units. Due to this generic software design neither the database nor the data type of the attributes is restricted. The configuration is saved in protected binary format to allow the safe storing of database connection parameters like user and password. Using the generated configuration file the post processing framework will be able to integrate arbitrary data structures by connecting to a database, processing the available data and storing the results in the same or another database.
Figure 2.3-10 Mapping of coil/defect-attributes to database columns Therefore it is possible to use a common rule-base for all inspection systems of the plant independent of the ASIS supplier. If the usage is focused on process correlation or automatic grading by classification the priority of data integration should be the collection of a representative data set for analysis and a good training set to allow a grading approach as explained in Figure 2.3-11 respectively. Therefore at ILVA data concerning the year 2006 have been extracted from the ILVA AS400 computer system as MS Excel files, for SPL and CAPL production lines.
26
Two kinds of file have been set up: o
The files called GTFRED.xls, containing general coil information, the defects warned by the inspector and the final coil classification.
o
The files called DEVIATED.xls, containing information about the coils which have been deviated for some reason. There is a specific code which identifies coils deviated due to surface defects highlighted by the inspector.
Each coil has a unique identification number named IDNOVI. This allowed joining data among different files. Data structure for the development of coil grading software tool was as follows: o
ASIS data extracted from the HDs or DAT support (kind, number and position – bottom or top surface – of the defect, position of defects on strip surface in respect of fixed origin, size of the defects). These data have been extracted as MS Access files.
o
Data concerning coil characteristics extracted from AS400 (thickness, width, kind of surface, commercial quality, grade of steel, customer, final grading of the coil and, if present, kind of defect). These data are in GTFRED.xls file.
Final grading actually can be:
0 – The coil is ok for the original customer, there are no defects
1 – The coil is ok, without defects, for a customer different from the original one
2, 3 – The coil is down-graded or re-classified (there are two different levels of down-grading, according to the severity of defects: second or third choice)
9 – Scrap o
Quality data concerning processed coils sent to finishing lines to perform a deeper manual surface inspection and also other operations on the coil. This is the procedure followed by quality department inspectors in presence of a large amount of defects (from ASIS maps); at finishing lines it is possible:
to swap surface (from top to bottom surface) according to final customer needs
to make edges trimming (reducing the coil width) if defects are in this position and then to send coil to a customer different from the original one
to split the coil if defects are concentrated in the centre of the coil, in order to eliminate them
In this database it is also possible to have information about the final grading of the coil. These data are in DEVIATED.xls file. Thus the data integration at ILVA does not rely on automatic data collection, but on completeness of the generated training set and it is essential to integrate any information available as shown in Figure 2.3-11.
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AS400 (QUALITY DEPT DATA)
PARSYTEC SYSTEM
VARIOUS COILS DATA GTFRED.XLS (MS Excel format)
DEVIATED.XLS (MS Excel format)
DEFECTS DATA (MS Access format)
FILTERING CLUSTERING
COIL CLASSIFICATION DATA
PRE-PROCESSING (FEATURE EXTRACTION) CONDENSING
-
CONSISTENCY CHECK
+ AUTOMATIC COIL GRADING SYSTEM
Figure 2.3-11: Schematic representation of data integration at ILVA One of the main objective of the project is the improving the knowledge of defects and their causes. By the defect list class definition, with the description of general causes, results that many variables and information missing from the automatic acquisition of process control systems. That is because the line automation is designed to control the process (assuring the product production) and not the quality product. For this reason DLL has focused this activity to define a new list of process variables with manual acquisition by operators every shift-hours. This investigation started with the analysis of 8 line sections on HSM (FOL-Heating Furnace, DEC-Descailer, QTO-Roughing Mill, CQX-Coil Box, FINFinishing mill, ROT-Run out table, UFC-Ultra fast cooler and BOB-Coiler) for the identification of the points of measure with coil quality influence. The measures can be quantitative and qualitative. Usually this set of variables is missing in the process database The implementation of the Check-list was done by DLL. The implementation regards the definition of way and means for such data acquisition. For every section was prepared a paper form with the list of variables to check and the range of their acceptance. Table 2.3-3 shows the check-list for QTORoughing Mill. The operative form contains also information about the operator that performed the line inspection. The form is filled manually and after reported in excel file. The QTO check-list was the first list implemented and its data was used during the correlation analysis. During the project the other check-list were implemented and the complete monitoring at the end of project is ready. The application of automatic procedure for electronic management of these data is not complete because this approach is not managed by Quality staff involved into IRSIS project but by the Process Staff. Anyway the available of this data is guaranteed with a manual procedure of import on the quality database.
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Process parameters
Target range
Temperature of top-roll
30-70 °C
Temperature of bottom-roll
30-70 °C
Difference of temperature of rolls
5-10 °C
Vertical stand movement
0 mm
Maximum value of movement
0 mm
Transfer table (electrical)
All
Scale presence after 3rd pass
No
Roughing passes
1-3-5-7
Descaling pressure of ramp-1
90-150 bar
Descaling pressure of ramp-2
90-150 bar
Descaling pressure of roughing mill
up to 90 bar
Number of ramp used
2
Transfer table rotation (mechanical)
All
Difference of Temperature of 2 pyrometers
5-12 °C
Coil camber
No
Table 2.3-3: List of variables and its target in Check-list for QTO-Roughing Mill Thus ASIS reports, quality operator inspection, process variables and status description of process sections (check-list) have to be considered in order to build a new database (named IRSIS) for more easy quality data manipulation. Indeed the actual database is organized for process lines (database of level 2 of automation) and for customers. The through-process view for quality monitoring is not yet completely organized. The main idea of data integration is shown in Figure 2.3-12 where the general modules architecture is described. The schema is composed by a new database (so called DB_IRSIS) where the data is loaded with automatic procedures, where is present a connection (Parsytec system and HSM process db), and manual operation for “check-list” implementation and data import for other source (i.e. Slab supplier). All the system is supervised by software application (so called irsis.exe), developed by CSM. The software was implemented in C# language and installed on plant workstation (named CSM-ICOE) with Windows Server 2003 and SQLserver 2000 as db. The automatic acquisition is performed by software n the basis of the configuration of “Scheduled Task” service. Once a day the software runs in “AUTO” mode (user interface disabled) and it collects, all data of coil processed during the day before. For the operation of data extraction the irsis software provides of an operator interface. The main screen-shot of program is shown in Figure 2.3-13 and it is divided into three main section. At the top section the operator write the coil identifier, process range time, grade. Into the middle section the operator chooses the process variables, grouped in subset, and defect classes. In the lower section there is the output results of query on database filtered with the parameters introduced before. The request could be done on single or groups of coils. Finally the results could be exported to text file in order to use it with the data mining software. The software is generally used by CSM to select and prepare data for correlation analysis. In addition the programs provides further function to query the database status (number of total coil archived)
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Figure 2.3-12 General architecture of data integration developed
Figure 2.3-13 Main panel of data gathering and extraction software tool
2.3.2.2
Task 2.2: Acquisition campaigns of related process, customer/order and defect data
Aim of this task is the generation of a data basis usable for method development as well as for method evaluation. At SALZGITTER presently the data from surface inspection are extracted from the ASIS databases automatically by extraction software on Java basis. The software realises, if a new coil was inspected, then it will be processed and transferred to the MySQL database. The post processing algorithms of the defect data after transferring are integrated in the software package and are activated immediately after transfer to the MySQL database. Data from the production process are presently available only for single processing lines. No tracking of the whole sequence of processing steps is possible. This will be changed in future with the setup of a
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data warehouse. Due to the economic breakdown at begin of 2009 the setup of this data warehouse was postponed, so that it was not usable in the last period of the project. Presently defect analysis with process and ASIS data is possible only on the stage of single process steps. Data of the customer and steel grade are presently available in databases related to the production quality department. Here the relevant customer data, information about the order and the planed application are stored. On a first view it seems to be useful to integrate this information in the MySQL database. During this project it turned out, that the inspection sensitivity, related to three different quality grades, gives as well a good indication of the required surface quality for a specific coil. It contains for each quality grade a different setup of the allowable or tolerable defect types and sizes and detection and classification parameters. This will also include coils that have no dedicated order and customer but could be used for arbitrary customers if the quality grade is fulfilled. During the project period the data of more than 31000 coils have been collected in the inspection data base. These data were used for development and testing of post processing algorithms. Due to the amount of data, the inspection results only from HDG2 in Salzgitter were used presently. Because of the same system architecture and the same defect catalogue the extension to data from HDG1 could be done rather easily. Figure 2.3-14 shows the databases acquired during the course of the project at ILVA. Skin Pass Line
SPL DB 2006
Continuous Annealing Process Line
CAPL DB 2006
Hot Dip Galvanising Line
HDGL DB 2008
Jan-Mar
Figure 2.3-14: Databases collected at ILVA First of all two main databases of ASIS data have been acquired; One referring to SPL and one to CAPL. Due to the data storage arrangement available at the beginning of the project, such data had to be extracted from DAT using a proper device. Once they have been transferred to HDs in MS Access database format, the acquisition activity could have been considered as concluded. An acquisition campaign has been done also for the ASIS at HDGL (making use of the system upgrade performed at the end of 2007): a database containing Parsytec data (MS Access format) in the period January – March 2008 has been downloaded. For the reference period, also the info about quality/client/order and the available process data for each processed coil have been collected in one Excel file. The so created HDGL database has been analysed in order to find useful correlations. Unfortunately, some process data, such as air blade work distance or temperatures of ovens and steel sheets were not available at the moment in which these activities have been carried out. Also, some process data were not stored in the computer system, but only viewed real time. Nevertheless, the process data available were as follows:
Date Coil ID Coil size and weight Steel grade Thermal cycle Coating thickness (set and measured) Process speed
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Skinpass setting Tension leveller setting Final mechanical properties
Some other important parameters (Zn bath temperature and % of Al and Fe in zinc bath) are usually measured twice a day, so they have been considered constant during the period between one measuring and another. ARCELORMITTAL ESPANA has started its data acquisition campaign, taking care from the coil evolution and drawing the entire coil‟s history including rotations, cutting and welding processes and many other specific aspects. For data mining and within the project only coils were used that were not cut, thus a 1:1 relationship between HSM and PL result should be obtained. This implied that several coils firstly collected were not useful. This particular task was very time consuming as no general gathering process is produced, but specific selections are done in order to avoid divided coils, data holes on process variables and many other specific criteria. BFI gathered data from ArcelorMittal Eisenhüttenstadt hot strip mill (HSM) and galvanizing line (GL) for the investigation of defect evolution across the two production steps. The database contains data of more than 30.000 coils with 40.000.000 defects of the two inspection systems. All data came from production of the first semester 2008. The activity of data collection at DUFERCO was started after the completion of the survey of the plant situation with the help of implementation of tools developed by CSM and the integration of check-list variables This activity was carried out during the second and third year of project because this task was the support of the all work of correlation analysis as focused into the following work packages. Two main test set was prepared in order to investigate the correlation process-defects. The first one was from Sept 2007- to Jan 2008 (called Sample_1 in Table 2.3-4), while the second one was from May 2009 to June 2009 (called Sample_2 in Table 2.3-5). This is because the results of the first set of data has brought partial results but important information for the preparation of the second set of data. The following table summarize the numerical parameters of collected data.
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Period
Sept 2007- Jan 2008
Sample_1
Coils selected
24177
Total coil available with classification confirmed by quality operator
Coils without defects:
22138
(92.0%)
Coils with defects:
2039
(8,0%)
OK RISK 1618
(6,4%)
The grade of defect is light and the decision is to continue the procedure as good coil
CUT 158
(0,6%)
Is necessary the coil head/tail cutting
NO OK 262
(1,0%)
The coil is rejected
Defect class selected by operator
SHELL
4 sub-classes of different shells (filiform, double skin, comet, scratch)
ASIS variables
26
Data from ASIS report (number of defects for each of 13 classes for top and bottom surface)
Casting process variables
60
Data provided by slab producer CARSID
Roughing Mill process variables
15
Data from Check-list
Finishing Mill process variables
230
Data from process control system
Table 2.3-4: Description of Sample_1 All data from continuous casting has been supplied by the slab producer (CARSID) and available in text file. By manual procedure the data was transferred into IRSIS-DB. The main Casting process variables available for Sample_1 were the following: • • • • • • • •
identification of heat, sequence and line of process, chemical composition, casting speed (min-max, standard deviation), mould frequency (min-max, standard deviation), mould level (min-max, standard deviation), powder type, temperature of liquidus, process events (type and numerosity)
The process event is the description of a non-standard condition that happens during the casting process. Usually the event is correlated to a process variable that assumes a value outside the regular range. All the critical conditions are collected into a list and coded into about 100 different types. Typical process events are: casting speed variation, mould level fluctuation, powder level reduction, first slab of sequence; no nozzle protection and so on. The main Hot rolling process variables for Sample_1 were the following:: • • • • • • • • •
temperatures of heating furnace, slab dimensions, pressures of descailer (oven and roughing mill section), number of roughing mill passes, rolls forces (min, max, standard deviation of every stand -six in total), coil temperatures after mill. coil temperatures at coiler coil dimensions defects detected by Parsytec system
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The defect data from Parsytec system is reported as total number of defects for each of 13 classes both top and bottom surface. No additional information for this first campaign was examined. Period
May 2009- Jun 2009
Sample_2
Coils available
6931
Total coil available for analysis
Coils selected
802
Classification by quality operator
Coils without defects:
670
(83,5.0%)
Coils with defects:
132
(16,5%)
OK RISK 44
(6,0%)
The grade of defect is light and the decision is to continue the procedure as good coil
CUT 40
(5,0%)
Is necessary the coil head/tail cutting
NO OK 48
(5,5%)
The coil is rejected 4 sub-classes of different shells (filiform, double skin, comet, scratch. 3 sub-classes of different scale (salt&pepper, sagger, M scale)
Defect classes selected by operator
SHELL SCALE
ASIS variables
4 (each defect)
Data from ASIS database (for each defect of coil: classification, position downweb-crossweb and size)
Casting process variables
0
No data available from slab producer.
Roughing Mill process variables
15
Data from Check-list for QTO (Table 2.3-4)
Finishing Mill process variables
21
Data from control system, selected by process technicians
Table 2.3-5: Description of Sample_2 In addition to the variables of Sample_1, the description of defects on coil is more exhaustive into Sample_2. With the direct access to ASIS database each single defect is available to correlation analysis (type, position and size). For this sample additional elaboration, based on these defects features, was done as described into the following chapters. During all the data phase the acquisition has been supervision by quality operator in order to validate the data because every unwanted error can be transfered to the analysis of causes-effect with unexpected results. This attention is used at this stage because the acquisition campaign is very critical point and the software acquisition tools are not yet complete for the on-line use. With the future implementation of automatic tools for data validation the operator presence will gradually decrease. The supervision task by quality is been performed with the inspection of every coil from the Parsytec data storage. All defect images are inspected and confirmed selecting only the coil with presence of typical defect with casting cause. Every coil is classified into 4 classes: OK
for coil with no defect.
OK-RISK
for coil with very light defects presence.
CUT:
for coil where the cutting is required to remove the heavy defects.
NO OK for coil with general presence of heavy defect.
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This kind of division of data samples was using during all project as operator confirmation of defect. This approach shows 2 main classes OK, NO-OK that represents the presents and absence of defect when the other two classes are used as sample of light or restricted presence. During the data collection task and mainly after the first round of analysis performed with Sample_1, DLL has carried on an acquisition campaign of defect samples for classifier training. For this reason a new classification tools by Parsytec is now used. The tool is called CBE (Classification Build Environment) and it guides the quality operators to quickly prepare a solid defect samples for classifier tuning with a big time reduction. Besides the classifier performances checking is also performed by the same tools. This activity is justified by the mandatory of reliability of ASIS results. Among the various problems related to the preparation of the data and reached a long period of low production, mainly linked to the economic crisis, loss of significance of the data collected This ongoing activity data collection of reference to the classification was necessary because the maintenance of the performance of classification is crucial. Indeed the correlation between the defective detected by the system of inspection study the status of the process relies on the accuracy of the results of the same and therefore a continuous supervision of the quality is necessary for the assessment of the same classification. 2.3.2.3
Task 2.3: Definition of quality measures
As basis for the usage of ASIS data, application adapted quality measures have to be defined. Therefore different approaches were chosen for testing within this project. For the correlation of HSM and PL inspections and the description of scale affection a quite straight forward approach was selected:
track scale defects arising from HSM, estimate area and relevance
estimate the amount of scale removed in PL
"measure" cleaning capability of PL for some operational conditions by correlation of the results.
So, by tracking relevance and area of defects remaining after PL it is possible to identify defects that are acceptable for a given quality level at finishing facility. The first problem to address is how to represent different types of defects on the coil surface due to thermal variations within the coil under variable positioning, as well as variations in the positioning system itself. Another aspect to consider is the accumulation of damage arising from different defects located closely together, as well as the different significance of different defects, according to quality criteria desired by the customer. In order to be sufficiently tolerant and flexible, it is proposed to use a Gaussian function for each defect. These functions take the form:
(eq 1) for some real constants a > 0, b, and c, where a is the height of the Gaussian peak, b is the position of the center of the peak and c is related to the full width at half maximum (FWHM) of the peak according to:
(eq 2) Gaussian functions are among those functions that are elementary, but lack elementary antiderivatives. Nonetheless, their improper integrals over the entire real line can be evaluated exactly:
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ax e dx 2
(eq 3)
a
It is proposed to consider an axis that is perpendicular to the coil to measure quality damage. In fact, due to the nature of our particular application, authors propose to use a two-dimensional Gaussian function representing each defect found. Its elementary formula is:
(eq 4) Here the coefficient A is the amplitude, xo,yo is the center and x , y are the x and y reach of the blob. In general, a two-dimensional Gaussian function is expressed as: (eq 5) ab where the matrix bc is positive definite. For the general form of the equation, the coefficient A is the amplitude and (x0,y0) is the center of the blob. The lateral extension of defect influence is controlled by the variance along each axis. Extension of this elementary approach to all defects in the coil will produce a function representing defect‟s influence (see Figure 2.3-15).
(eq 6) where CDrepresents the set of defect classes with relevance in this particular problem and Nirepresents the number of defects classified into class i found in this coil. By integration, it is possible to qualify damage for the coil as a whole:
(eq 7) Size of coil is LX m length by LY m width. The proposed approach makes it possible to support different quality sensitivities just by changing parameter A or variances along the axes in (eq 1), according to the user‟s interest or the relative relevance of each defect type. For having a common approach a defect it is modeled like a Gaussian function, showing the defect‟s influence area. A large dataset of defects is considered by adding their Gaussian functions to a global defect map of the strip. Comparisons between facilities can be made by comparing these global defect maps. It also is possible to produce and measure iso-surfaces according to defect intensity conditions (see Figure 2.3-15).
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Figure 2.3-15 Defect density approach based on Gaussian diffusive concept for comparing coil maps.
Number of coils
Number of coils
Besides that continuous approach a quality measure can also be a scalar value usable for coil grading applications. One quite common measure is the “number of defects” for some prominent classes. To assess the usability of this measure alone, the ILVA databases described in 2.3.2.1 was used for an evaluation: At first, for each process line and defect type a grouped frequency distribution of the discrete variable “number of defects” has been calculated. Several diagrams have been done, in which the x axis represent the number of defects class (e.g. 0 to 100, 101 to 200, etc.) and the y axis the number of defective coils (blue line) or non-defective coils (green line) for each number of defects class. If the number of defects could really be considered as the most important parameter during the decision phase, it was expected a distribution as showed in Figure 2.3-16. Analysis results pointed out that the distribution for defective items and not defective items are quite similar (Figure 2.3-17).
Number of defects
Figure 2.3-16: Theoretical curve (green line: non defective coils. Blue line: defective coils) in case of the number of defects were the main parameter to take into account during coil allocation
Number of defects
Figure 2.3-17: Qualitative real curve (green line: non defective coils. Blue line: defective coils), in which the number of the defects is not strictly related to the defective or non-defective status of a coil
This means that other parameters had to be considered. For example: defects size (large defects should have higher importance) defect position (defects on border could be cut away) distribution on the surface (defects could be concentrated on a certain area or uniformly distributed)
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Anyway, the possible performance of this kind of evaluation has been estimated giving a proper threshold value of the variable “number of defects”. The maximum of this parameter (i.e. the achievable accuracy at the best threshold value in order to choose between defective or non defective coils) ranges from 50% to 80%. That is to say that 20% to 50% of processed coils would be misclassified and wrong allocated: therefore, this kind of data treatment has been bypassed and ILVA attention has been focused on a different approach. The Parsytec system provides a map of the defects found on the strip surfaces including their type, position and area. The ILVA grading tool pre-processes the information from the Parsytec database and extracts the following features for each coil and for each type of defect found on it: Defects maximum area Defects average area Defects area standard deviation Defects maximum longitudinal length Defects average longitudinal length Defects longitudinal length standard deviation This set of characteristics has been selected as it emerges that human operators in most cases bases their decision on such information when determining which class a coil belongs to. The pre-processing phase can be subdivided in the following steps:
Filtering. Not interesting defects and defective information have been discarded.
Clustering. The aim is to identify concentration of small defects. It has been performed only for some defect types. Clusters to be considered as new defect types. Not implemented in the working version.
Consistency check. Not meaningful data have been discarded.
Condensing. A coil could have thousand of defects. The classifier does not receive the information on each single defect but uses statistical information for each defect type: mean, maximum and standard deviation of selected defect size parameters (area and length, as previously mentioned).
The pre-processing work revealed to be time consuming due to the huge amount of data to be verified and processed. With respect to the previous investigation that for advanced coil allocation it is not sufficient to regard the number of defects it can be stated that the automated evaluation of defect maps has to be done according to several border conditions. Type of defects, their size, configuration, density and other information has to be collected and stored in a table as features usable for classification and modelling. A quality assessment by operators uses besides fixed defect limits ad hoc decisions respecting also border conditions not to be fixed in dedicated rules. Therefore at SALZGITTER a definition of quality measures was discussed with the production department before the setup of the evaluation procedures. To come as close as possible to the decisions of the operators the main features for evaluation were identified and agreed. Main result of the discussion was the separation of defects in three different severity classes. Critical defects for all surface grades were all severe defects according to Table 2.3-6. Only few and isolated defects of this type can be tolerated. Each individual critical defect has to be visible in the visualisation and to be respected in the evaluation table without being dropped or condensed to groups. Medium and light defects according to Table 2.3-6 can be condensed to groups or defect agglomerations or be dropped if a certain density is not reached. Uncoated regions are a special case which showing up only in small groups, so a specific number of them will be tolerated. Geometric configuration of the defect groups, its content of defects of different classes, the summed up area, length and width and the size of the group are important features and has be recorded for later use. Critical defect sizes and frequency for a number of defects types were discussed but not used. As a result the number of defects in the specific defect classes instead has to be determined and stored in the evaluation tables as features for classification. If the tables are used for training of classification models for coil allocation and assessment, the tolerable number of defects of a certain type will be determined by the classification algorithms. So the right quality measure has to be found during the project, while
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one task of post processing is to deliver the appropriate features for a good separation between released and stopped coils. The data evaluated from the inspection master and individual defect table are stored also in two evaluation tables with individual information about each critical defect or each defect agglomeration and summary information about the specific side of the coil in one table row. For classification only the content of the summary table was used. Severe Defects Holes Rokes Scabs Slivers Laminations Scale
Medium Defects Skin pass defects Rolling defects Roll marks Scratches Dross Uncoated regions
Light defects Dirt Feathers Oil Spots
Table 2.3-6: Severity classes for HDGL defects at SALZGITTER The DLL and CSM approach to quality measures involves the features of defects of Parsytec classification. The main quality measure, used at first by coil allocation, is the defect frequency. A numerical threshold is used to qualify the good coil. This quality measure was used into Sample_1 data were all correlation analysis was done taking into account mainly the defect presence. However the first approach to correlation analysis has shown that the use of defect frequency is not satisfactory. For this reason new quality measures was introduced into Sample_2 data. These measures involve the spatial approach with the definition of defect distribution. These quality measures are also a valid help to the customer allocation because highlights the good areas for following cutter. In order to calculate the new coil defect quality parameters the coil was divided into 5 main zone: 2 Border Stripes with 5 cm in width, the Tail and Head section of 10 m and the Body as remainder area (Figure 2.3-18). For every defect class the following quality measures are calculated: Total defect counting on each area. Distribution density at head, body and tail of coil Cross wide distribution (edges and centre) The dimension of head tail and edge is defined by commercial specification related to the maximum possible coil cutting that assures the customer order.
m
)
Tail coil (10 m)
t ri p er S
rip
Body coil
Bo rd
t rS e d
m
(0 ,0 5
)
5 ,0 (0
r Bo
Head coil (10 m)
Figure 2.3-18: Definition of five coil zones Border strips, Head and Tail of coil are critical area for defect detection, special defect classes are typical in these areas and the presence of defects is generally confirmed for all coils. The presence of defect on Body area is more representative. For this reason the Sample_2 data was build only with the coil Body defects.
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2.3.2.4
Task 2.4: Implementation of task supporting tools
According to the work programme two main types of tools were developed within the project: A vendor-independent post-processing framework and tools for coilmap comparison of different inspections of the same coil. 2.3.2.4.1
Post-processing framework
At BFI the implementation of a vendor independent post-processing framework has been finished successfully. The latest version includes the configuration tool mentioned in 2.3.2.1 and the postprocessing tool itself implemented as command line application. The post-processor is rule-based and there are 3 types of rules implemented: Single rules Context rules Recursive rules Please refer to 2.3.3.1 and 2.3.3.2 for detailed explanation of the rule types One major problem that had to be solved before on-line application was the performance of the recursive approach. The performance target of the framework is “coil-realtime” meaning that the postprocessing of a coil may take at maximum as long as the coil is processed at the line (at SALZGITTER HDG approx. 20 minutes). The first implementation using MATLAB took 36:16 minutes for one clustering rule on a test coil, which could be reduced to 03:47 minutes, meaning that about 5 rules should be possible using MATLAB as rule language. Thus a new approach was selected using rules written in C# language that finished the processing of the test coil in 8 seconds and therefore allows post-processing with more complex rules. One important feature that was implemented within this project is the integration of arbitrary quality measures, which can be calculated within the body of each post-processing rule. These measures can be added to every aggregated defect, generated by a post-processing rule. Common examples are the count, maximum area or mean length of the single defects merged to the aggregated one. The result is stored per-defect in an additional table in the database and thus can be easily accessed for later evaluation. This flexible approach allows simple integration of new quality measures in the post-processing system without change of the table structure. Finally a special operation mode was implemented to allow the preservation of merged defects usually not stored in the target database. Surely this approach won‟t lead to a reduction of the data, because it will only generate aggregated defects without discarding the single ones, but it is helpful during the test phase to learn more about the effects of each single aggregation rule. In operational mode this features can be switched of and the amount of defects will be reduced as intended. 2.3.2.4.2
Coilmap comparison
As mentioned in the midterm report the investigation of defect evolution over production steps can be divided into two defect types: -
Single defect mapping (like heavy shells, holes, etc) Area defect mapping (like scale, dross, etc)
Regarding the different characteristics of these two types different tools were developed within this project: Area defects According to the output of Task 2.3, ARCELORMITTAL ESPANA has developed a graphical tool, both, for uses of technical people at quality department but, even for reliability during internal analyses related to sensibility of ASIS located at different facilities. This tool contains the algorithm for congruence of defect mapping across facilities by evaluating the common area shared by Gaussian functions defined over defect areas mentioned in 2.3.2.3
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So it becomes possible to track the cleaning capability of the PL as well as the operational conditions and to compare the specific size of defects on specific areas (or meters inside the coil) keeping into account other parameters like chemical composition and cooling rates. To make these operations very easy and useful for technical people related to the quality departments they were provided within a web interface. This tool includes the number of defects of every coil, as well as other parameters. The tool was mainly oriented to visual analysis and interpretation in such a way that some analytical indicators were also calculated but where the focus was the global understanding of the cleaning process itself in connection with the quality of the surface coming from the hot strip mill (see Figure 2.3-19).
Figure 2.3-19.- Main pictures for one particular coil showing scale defects at the exit of the HSM (left pictures) and after pickling line (right) and for the upper face (first row pictures) as well as for the opposite face (last row pictures). Color points represent different types of scale defects. Several facts were observed: a) Difficulties by tracking position of one particular defect point along the coil itself as its length varies after being processed by the HSM ASIS, due to cooling process (see length reduction on Figure 2.3-19, right side pictures with about 600mm of reduction),. b) Different sensitivity levels found by different ASIS system that makes appear unknown defects after pickling (see Figure 2.3-20). c) Very different scale defect level, even when it is processed same type of material, between coils.
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Figure 2.3-20 Image showing how new unforeseen defects are identified by pickling line (upper right corner). Single defects To develop specialized mapping algorithm for single type defects a modular concept was realized to allow easy integration of different methods (Figure 2.3-21).
Figure 2.3-21: Concept of single defect mapping solution In order to realize this concept BFI implemented a modular data processing framework as prerequisite for the systematic investigation of single defect mappings across production steps. Within this project this tool was developed to investigate defect evolutions from hot strip mill (HSM) to galvanizing line (GL). This mapping should be possible in upstream and downstream direction. The modular concept allows the expansion to more than two lines to integrate further inspection results of the same coil. It was determined that a quick data access and an intuitive handling and visualisation of all relevant information are key points for the acceptance of the developed solution by the quality personnel. Therefore adapted to the specific need of the different users of the system 3 different use cases where developed in order to track quality problems and optimize the upstream process. The first two use-cases are mainly focus on visualization for manual evaluation whether the third one is focused on the automatic analysis of multi-inspection ASIS data. The visualization applications display top and bottom view of two inspections side by side. One of the involved systems (HSM or GL) has to be defined as reference and all parts of the reference coil that where inspected by the other ASIS are
42
transformed to a length correct visualization. In other words the reference system is shown as inspected whether for the other system all rotations, unwinding, cutting and welding operations between the two inspections have to be inverted to come to the same basis for defect positioning. Besides the defect map it is also possible to select defects in the maps to display the images as it was observed that a key point for the visualisation of ASIS data is the integration of defect images. For the user of the solution it is very important to see images of the coil surface to get an idea of the actual surface quality and severity of single defects and to verify the output of the developed solution.
Figure 2.3-22 Screenshot of defect mapping solution The following use cases where developed within this project: 1:n mapping: Selection of 1 coil from source system shows the parts of the coil inspected at the destination system. The selected coil is reference system for defect positions and the defects of the destination system are transformed back this use case is especially useful for the tracking of customer complaints for single coils. n:m mapping: Same as 1:n mapping except that more than one coil can be selected. The selected coils are displayed in production order of the reference system. Therefore this mode is especially useful for evaluation of quality aspects of certain production periods Multi-coil analysis: To gain general knowledge about evolution of defects across production steps and generate quality rules a huge amount of coils has to be evaluated. Automatic correlation analysis can process thousands of coils with millions of defects to generate quality rules. The results of this automatic analysis is stored in a database for further analysis. 2.3.3
WP3: Development of data utilisation procedures
WP3 was the central work package of the project providing fundamental work to allow enhanced ASIS data utilisation. By application adapted post-processing the data basis could be improved. The results of WP2 were used to gain information about the life cycle of defects, knowledge about defect emergence and transformation during production process.
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2.3.3.1
Task 3.1: Use post-processing to improve data reliability
The use of post-processing for the improvement of data reliability can be divided into two types of rules. Single rules: Using a condition part and a script part. As variables any defect and coil attribute stored in the respective database tables can be used. For each coil and each defect all conditions get evaluated. If the result of one condition is true, the script is executed for the current defect. As well changing defect attributes as creating new defects is possible, considering the correct allocation of new unique defect IDs. Thus this rule type is able to fulfil simple verification rules depending on the defect and coil attributes itself (like “No defect X on material Y”), but there is no use of context information possible, required for clustering and condensing of defect data. One way to detect rules of that type automatically is the use of statistical evaluations. Within this project it turned out that most of the rules of the single rule type that could be used to improve data reliability are already covered by the trained classifier itself. Therefore in industrial usage this type of rule is mainly used to cover rules of the internal process (“all shells smaller than area x should be deleted”) or delete isolated defects that are not classified as severe. Generally it can be stated that post-processing rules require context information to bring added value to the classification, because only in that case it can use additional context information not available for the automatic classifier. Context rules: Using the same procedures as single rules this type allows also the definition of a context rectangle around every defect. In the script part not only the defect attributes itself, but also all defects within the defined region can be accessed and thus rules like “more than 10 scale defects in the neighbourhood, than also scale” can be realized. Some defect classes with overlapping classification could be identified and reclassified using these context rules. The algorithms have to be setup using fixed rules that have to be detected and verified by analysis of the images of defects in agglomerations. Another test could be the distribution of defect classes within the context. Including context conditions in post-processing rules raises another problem. The change of one defect may influence the context of another defect and thus change the behaviour of a context rule (Figure 2.3-23). To solve this problem a fixpoint iteration approach was implemented. One coil will be postprocessed as long as changes occur (stopping at a maximum iteration count). This approach will ensure that the result of rule application is as expected. First test showed that this approach is feasible, because less than 1% of the processed coils needed more than 10 iterations until a fixed coilmap was reached.
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1. Iteration
2. Iteration
Figure 2.3-23: Example evaluation of context rule: “If more than 2 triangles in context, then also triangle” (context represented as dashed square) The use of post-processing to improve data reliability is especially useful for classes with insufficient classification rate. Severe defects like shells and holes having a classification rate above 80% over all have to be excluded from post processing. These defects are of such importance, that they have to be recorded in every case. The improvement of the data reliability is reached in two different ways. At first the aggregation of the single defects leads to a more clear indication of severe defects and the geometric relationship to the other defects can be indicated. In any case a severe defect like a hole or a shell, isolated or in a small blob is more reliable as this defect within a large stripe with many other defects in Y or X direction. In this way weighting factors for the severe defects can be introduced and used for classification. Another test is the distribution of defect classes within the aggregated defect area. Some defect classes with overlapping classification can be identified and reclassified using this distribution functions. Additionally a statistical evaluation of the distribution of defect types or the deviations from the mean values of length, width or area within a stripe or blob can be done for a differentiation of different stripe or blob types. A detailed analysis of these options had not been performed. Several examinations of this differentiation showed, that this would increase the number of features for classification and modelling, but would not lead to a gain of reliability. A systematic analysis is also prevented by the lack of reliable data from operators regarding defect agglomerations and surface features across large scales. 2.3.3.2 Task 3.2: Use post-processing to condense defect data Another intension of post processing is the condensation of defects to large collections and to clarify the defect map of inspected coils. A simplification of the coil map is important because at SALZGITTER the number of defect indications on a single side of the coil has a range from 0 to 20000 with an average number of about 500. So a detailed analysis of all defects on every coil is not feasible. A method has to be found to indicate clearly severe defects, show defect aggregations and their geometrical relationship without loosing too much information and to keep information about the background of single defects or indications not integrated in larger defect collections. In any case a significant reduction of the number of defects and a clearly structured coil map is the aim that has to be reached. Therefore in a first step some apparently simple features in the defect maps on large scales up to the whole coil length have to be recognized. These are in detail lines of defects in rolling direction like scratches, dirt or other surface features that can be condensed to only one defect region. Another type would be defects in a line perpendicular to the rolling direction like welding seams or other defects across the strip width, which are normally split up to a multitude of defects. Additional types of defects will be blobs or areas of defects being concentrated on certain regions of the coil. For these defects simple neighbourhood criteria were used for grouping and condensing all found defects to one large defect region. All severe defects have to be individually recorded and stored. These defects are separated into single severe defects isolated on the coil or severe defects in stripes or agglomerations. If a severe defect is part of a defect agglomeration, this is specially indicated and can be used as a special feature. All defects rowed up in lines parallel the rolling direction were examined on periodicity. Not only small periods of several meters were respected but also larger periods of more than 100m. Each stripe in rolling direction containing periodic defects has to be marked as periodic. Single isolated defects on the surface belonging to light or medium defect classes should only be counted and dropped from the defect map. For dropping several neighbourhood and other criteria have
45
to be fulfilled like large distance from the next defect, isolated position, position not in stripes crosswise or parallel rolling direction or blobs and belonging not to a severe defect class. Besides the number of isolated defects the minimum, maximum and mean defect area has to be calculated for all background defects. The type of rules explained in the previous chapter can easily be used for verification, but for clustering and thus condensing defect data it is often needed, that you already know the complete cluster when the rule is applied. Therefore for the technical implementation of the above aggregation strategy BFI has chosen a recursive context selection approach. Recursive rules: Using the same procedures as context rules this type differs in the way the context is collected. If a defect within the context fulfils a certain criteria (e.g. same class, same side…) the context of this defect will also be included in the search. This procedure will be continued recursively until all defects reachable are included in the context. Using this complete context the rule is executed.Figure 2.3-25 shows the result of a post-processing of one coil with one recursive rule merging all defects of a certain class within a context of 10 m downweb and 10 cm crossweb to one new defect. Starting from the upper coilmap the result for all iterations is shown until the fixpoint was reached. In this example the amount of defects could be reduced by a factor of 4 without loss of information. Another important type of defect consists of single defects with periodic distances. Generally all Y defects were examined for periodic defects. In many types of Inspection systems there is a maximum length for the test of periodicity. The software implemented will find periodicities even on very large scales. So detection of roll defects in previous production steps like hot rolling with large periodicities is possible. All relevant details of these collections are stored together with the geometrical data of the defect collections. One interesting detail e.g. is the number of severe defects medium and light defects and the number of different defect types within a collection. In the same way the relation between the accumulated defect area and the collection area, giving the average defect density, is evaluated and stored. Other important information like average values, standard deviation and minimum / maximum values of defect areas, distances, length and width are presently implemented. The post processing software was integrated in automatic software, which is activated by a time schedule. If a new coil was put into the database, the coil is processed and the processed data were stored in the result table. Basing on the results a collection of rules for coil grading has to be applied to classify the coils in several quality groups. This decision system was developed during the project. With the production department several rules and setups for the boundary conditions were tested before implementation in the software.
46
Figure 2.3-24: Automated transfer and post processing of the ASIS data, detailed Software diagram for the ASIS application given in Figure 2.3-8
Figure 2.3-25: Post-Processing result for defect aggregation (10 m downweb, 10 cm crossweb)
47
Figure 2.3-26: Typical defect map of two different coils; left – right: strip width; top – bottom: strip length Red boxes: Severe defects Green boxes: Y-stripes, Yellow boxes: X-stripes Blue boxes: Blobs Two examples of typical defect maps processed with the complete rule set are shown in Figure 2.3-26 where the reduction was even higher. The defect distribution and additional information about the defects can be accessed by web based visualisation software from all PC‟s that have permission. This software was developed and tested and will be used for the visualisation of automatic generated coil grading decisions. In a final version integrated in the data warehouse the condensed defect distribution can be directly compared with the original defect distribution. Access to the images of the defects will then also be integrated for the most important defects. The parameters for the neighbourhood conditions are fitted to the defect numbers of normal production in a way that especially for coil maps in the region of 100 to 1000 defects the reduction of features sim-
48
plifies the coil maps to a large extend. For coils with several 1000 defects the reduction is not sufficient to really simplify the map, for these cases a different parameter set for the neighbourhood conditions have to be set up. The defect distribution and additional information about the defects can be accessed by web based visualisation software from all PC‟s that have permission. This software is presently in use locally. In the actual version the condensed defect distribution can be directly compared with the original defect distribution. Access to the images of the defects will be integrated later for the most important defects. In a second way for the verification of the post-processing rules the geometrical arrangement of defect positions on large scales becomes visible by agglomeration of the defects to X/Y stripes and blobs. Patterns on the strip becoming visible in this way and can be used for coil diagnosis without sticking too much to individual misclassifications of single defects. So the geometrical positions and extension of the defect collections and their content of severe defects are used as features for classification and modelling. 2.3.3.3
Task 3.3: Development of best practice data preparation approaches
The partners tried to summarize their experiences of data gathering and data preparation in a schematic best-practise data preparation guideline. This approach is divided in two parts: 1. 2.
Preliminary analysis Data collection
After the type of investigation is selected two independent paths should be followed: On the one hand for the preparation of ASIS data and on the other hand for the preparation of process data. This applies, because the nature of these kinds of data requires different treatment.
Figure 2.3-27 Schematic approach of preliminary analysis for data preparation In the first phase (Figure 2.3-27) of the preliminary analysis it is important to bring in as much a-priory knowledge about defect and process data as possible to ease the following data collection stage significantly. In this phase an important step is the selection of a common length basis (i.e. per coil or per coil parts of equal length). This common basis defines the required feature calculation step for the process and the grading measure for the defect data. The type of defect has to be characterised and a grading measure has to be selected to get from 2dimensional defect distributions on the coil to a suitable measure that can be correlated with the availa-
49
ble process data. A pre-selection of the significant process data is useful to reduce the amount of data that has to be collected. The major part of the data collection phase (Figure 2.3-28) is the plausibilisation of the gathered data samples. Whereas for the process data there are some standard procedures that can be used to verify the data and identify invalid values there are no methods for automatic ASIS data assessment. Therefore this part turned out to be very time-consuming, because the verification of the mass data of ASIS has to be done mainly manually by comparison with the human inspector and in-depth statistical analysis trying to discover outliers and possible systematic confusion with other defect classes. The next mandatory preparation step for the ASIS data is the post-processing as explained in 2.3.3.1and 2.3.3.2 to extract the required information usable for cause-and-effect analysis out of ASIS mass data. After the plausibilisation the verified data has to be processed according to the feature calculation defined in the preliminary analysis and filter conditions can be applied to focus the investigation on certain kind of data or to focus only on the worst and best samples
Figure 2.3-28 Schematic approach of data collection and preparation Practically as a result of investigation of the current situation the project needs 2 main software tools. The first one regards the collection and preparation of data and is strongly related to the data situation at the plant, while the second one deals with the application of data mining technology to define the main correlations between defect and process status. Here standard and commercial tools can be applied where for the first step custom software has to be developed. CSM has designed and developed new software tools for automatic gathering of quality and process data. For this kind of approach of correlation analysis (a lot of work made manually by operator) no automatic approach to preparation of data was performed. Because of the application of data mining different techniques the preparation of data was made in each case in different ways (application of filters, range of acceptance, training and test set preparation). The data-mining analysis was performed with a style laboratory. 2.3.3.4
Task 3.4: Process data correlation and emerging conditions of defects
Within this task the investigations where focused on different line types, because the available type of process data and the type of possible quality deficiencies between the lines is not comparable.
50
2.3.3.4.1 Hot Strip mill The technical approach to data mining was performed by DLL and DUFERCO with the main following methods which were intensively applied in addition to the standard statistical distribution description analysis. Dendrogram and Correlation matrix The dendrogram is the graphical representation of a statistical tool called “hierarchical agglomerative clustering”. Hierarchical clustering aims at defining a sequence of N clusterings of k clusters, for k Î [1,...,N], so that the resulting clusters form a nested sequence. The agglomerative algorithm starts with the initial set of N attributes, considered as N singleton clusters. At each step it proceeds by identifying the two most similar clusters and merging them to form a new cluster. This step is repeated until all attributes have been merged together into a single cluster. The similarity among the attributes is measured by means of the correlation coefficient which takes its values into the range [-1,1]: rho(x,y) = cov(x,y) / σx.σy where, cov(x,y) represents the covariance between variables X and Y; and σx is the standard-deviation of variable X. This tool is particularly interesting to: analyze and visualize the similarities existing among the attributes, detecting and eliminating the attributes (pruning) that are too much correlated (and thus bringing probably redundant information). Logistic regression model. In addition of the statistical distribution or data sample description the Logistic Regression Model is been intensively used for investigation activities. The model predicts the probability of event by fitting data with logistic curve (logit). In this case the event „presence/absence‟ of defect has been correlated with the process variables properly, by the selection procedures usually used in regression analysis. This relationship, expressed according to the link function of the linear regression (1), represents the probability (p) that the anomaly can appear. (1) Logit(p) = log(p/(1-p)) = a0 + a1x1+ a2x2 + ……+ anxn where a a0 , a1, a2 … an are the coefficients estimated by the regression and x1, x2 , … , xn are the selected variables
e a0 a1x1 a2 x2 ...... an xn p 1 e a0 a1x1 a2 x2 ...... an xn The function (1) defines a probabilistic model whose statistical significance has to be verified through the main statistical tests required in this type of analysis, with particular reference to significance tests on the model coefficients estimates. In the below model, the coefficients estimates are, both singly and simultaneously, significantly different from zero, with a significance level typically equal to 0.05 (95% confidence level). This tool is particularly interesting to describe the relationship between one or more independent variables (process features) and a binary response variable, expressed as a probability, that has only two possible values, such as defect (presence and not-presence). In the following paragraphs the list of investigation topics (A-D) carried on by CSM and DLL at DLL hot strip mill (HSM). A. Correlation focused on shell defect and casting process (detailed description of this analysis was done into DLL+CSM contribution report n. 4)
51
The main objective of this analysis was to find out the correlation between continuous casting variables and defects detected by Parsytec system and confirmed by quality operator. This test was performed on Sample_1 data focused on the SHELL defect (typical of casting process). The analysis approach followed by DLL was to start with all variables available and to use automatic techniques for correlation investigation. First of all the Dendrogram technique was used to reduce the number of variables because the set of 83 variables was correlated with index > 0,9 and these was replaced by 27 independent variables with a final reduction of 56 variables from the total. The Decision Tree was applied on a training set prepared with 320 selected records of bad coil (NOK+CUT) and 640 records of good coil (randomly taken from the OK) for a total of 960 records and 170 variables coming from the previous pruning.The DT results as following: Total DT information of sample set (= 42.92 %) : DT schema (33 nodes) TEST = 16 LEAF = 6 DEADEND = 11. DT test variables (Information as % of total DT info) : PROCESS_ALARM_80_(Manual Control) : 39.0 NORMALIEN : 15.2 CUSTOMER : 9.9 SCHEDULE_NUMBER : 7.6 REDUCTION_STAND_2 : 7.3 HEAD_END_AIM_GAUGE_OFFSET : 5.5 TYPE_TUBE_POCHE : 4.1 PRED_FORCE_STD_6 : 3.2 STD_GAUGE_HEAD : 3.0 The PROCESS_ALARM_80 (manual operation during casting) is the main correlated variable. By Decision Tree the correlation on slab produced in manual control during casting is important but not sufficient (total information 43%) Also the NORMALIEN variable (steel grade by chemical composition) and Customer have influence. With the same sample CSM has performed a parallel correlation analysis with a different approach, the first variables pruning was made with the analysis of histogram and technological consideration (knowhow on process) while the correlation was tested with logistic model. The histogram analysis has shown the following qualitative consideration: The powder and the casting line is not a strong correlation with defects. The casting sequence is important (the first and last slab) are more defectiveness The list of more promising correlated variables has been prepared Casting speed variation, Casting mean speed Oscillation mould Liquidus temperature (steel grade) Process alarm 10 (first slab in sequence) Process alarm 55 (risk of powder trapping) With the application of Logistic Regression to the list of previous variables, the prediction model of defect presence is about 50% while the non-defect prediction is more than 80%. This means that the model is tailored not for prediction of defect but it is an index of good quality The global correlation rules for shell defects were the following: The first and last slab produced of heat are most defectiveness The slab produced at manual control is more defectiveness The main process variables correlated are: casting_speed_variation, mean_casting_speed, mould_frequency and temperature_liquidus The correlation between shell and casting process is too weak in order to develop an automatic procedure but however is a good warning
52
B. Correlation focused on automatic classification and operator classification The aim was to find the correlations between ASIS classification and Operator classification. This means a classification performance validation of ASIS and confirmation of the reliability of data from ASIS. This kind of analysis was performed preliminary with Sample_1 with results not very encouraging because for the analysis was used as defect presence only the information about the total number of objects, for each class, recognized by ASIS (this information is too poor in order to apply a correlation method). A more exhaustive approach was performed on Sample_2 with more information from the ASIS defect presence. An example of this type of analysis is described below and It refers to the correlation between the features of Shell defect by ASIS, and the defect presence/absence confirmed by Operator. The eight features (both top surface and bottom surface described in Table 2.3-7), after a pre-analysis on restricted sample, are used to describe the single defect on coil (shown inTable 2.3-8). Variables NUM_DEFECTS AREA_MEAN AREA_STDV AREA_MAX POS_X_MEAN POS_X_STDV POS_Y_MEAN POS_Y_STDV
Description Defects amount Mean Area of defects Standard deviation of defect area Maximum of defect area Mean transversal position of defects Standard deviation of transversal position of defects Mean longitudinal position of defects Standard deviation of longitudinal position of defects
Table 2.3-7: Description of the features of defect used for the correlation analysis
DEFECT:Shells (bot- DEFECT:Shell lenghten DEFECT:Shells DEFECT: Shell lenghten tom surface) (bottom surface) (top surface) (top surface) NUM_DEFECTS_I3
NUM_DEFECTS_I68
NUM_DEFECTS 3
NUM_DEFECTS68
AREA_MEAN_I3
AREA_MEAN_I68
AREA_MEAN3
AREA_MEAN68
AREA_STDV_I3
AREA_STDV_I68
AREA_STDV3
AREA_STDV68
AREA_MAX_I3
AREA_MAX_I68
AREA_MAX3
AREA_MAX68
POS_X_MEAN_I3
POS_X_MEAN_I68
POS_X_MEAN3
POS_X_MEAN68
POS_X_STDV_I3
POS_X_STDV_I68
POS_X_STDV3
POS_X_STDV68
POS_Y_MEAN_I3
POS_Y_MEAN_I68
POS_Y_MEAN3
POS_Y_MEAN68
POS_Y_STDV_I3
POS_Y_STDV_I68
POS_Y_STDV3
POS_Y_STDV68
Table 2.3-8: List of features of sub-class defect of “Shell lengthen ” and “Shells” As first step, opportune data samples have been prepared by extracting a period of process-defect data from Sample_2. Starting from 6931 coils available, the sample was reduced to 4994 deleting out admitted range and missing records, and merging with Operator classification data, the sample further was reduce to 499 coil. To use in the best possible way the Logistic regression techniques, the sample was balanced to have half records of defect presence (“NOK”, “CH” and “OK RISQUE”) and half records of defect absence (“OK”). The final set data was of 150 records.
53
Table 2.3-9 shows the coefficients estimate (column Estimate) for the selected the features of defect and the relative influence (column Standardized Estimate) on the probability estimate. The greatest values in absolute value correspond to the more determinant parameters (In this case it is AREA_STDV3 ). Standardized Parameter
Estimate
Intercept
Estimate
-1.1525
NUM_DEFECTS_I68
-0.00307
-0.2748
AREA_MEAN3
0.000815
1.0867
AREA_STDV3
-0.00088
-2.1248
AREA_MAX3
0.000114
1.4408
Table 2.3-9: Coefficients estimate and influence degree of the parameters This correlation, expressed according to the function (1) of the probabilistic model, is represented by the probability that the defect can appear. The increasing of the probability estimated correspond to a more probability that the defect occurs. As shown in the Table 2.3-7, the probability estimate depends on defects amount of Shell lengthen (NUM_DEFECTS_I6), Mean surface defects of Shells (AREA_MEAN3), Standard deviation of defect surfaces of Shells (AREA_STDV3) and Maximum of surface defects of Shells (AREA_MAX3). The classification results of prediction model is shown in Table 2.3-10. As satisfactory results has been obtained, the aim not was to find mathematical functions able to quantify the prediction model but to understand if the real presence of defect is correlated with the features of defects by ASIS.
Classification Table Prob Level
Correct Event
Incorrect
Non-
Event
Event 0.5
55
51
Percentages
Non-
Correct
Event 22
22
70.7
Sensi-
Speci-
False
False
tivity
ficity
POS
NEG
71.4
69.9
28.6
30.1
Table 2.3-10: Classification table for the presence/absence estimate by statistical model Total record= 150 Sensitivity – percentage of correct event on the predicted ones 55/(55+22) Specificity - percentage of correct non-event on the predicted ones 51(51+22) The results of this analysis confirms the reliability of ASIS classification. C. Correlation focused on scale defect and process variables of HSM The statistical analysis was performed by CSM with data of Sample_2. This sample contains the process information with the means value for single coil and the defect information. In particular the process variables used for the analysis with the admitted range are described in Table 2.3-11 while in Table 2.3-7 the features of Salt&Pepper defect (kind of scale) are reported.
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Process Variable
Description
Admitted Range
MAX_FM_EXIT_TEMP
Maximum temperature of coil at Finish Mill exit
700-1000 °C
MIN_FM_EXIT_TEMP
Minimum temperature of coil at Finish Mill exit
700-1000°C
MAX_FM_EXIT_TEMP_HEAD
Maximum temperature of head coil at Finish Mill exit
700-1000 °C
AV_GAUGE_HEAD
Average gauge on head coil
1.5-15 mm
AV_WIDTH_HEAD
Average width on head coil
600-1500 mm
MIN_COILING_TEMP
Minimum temperature of coil at coiling
150-800°C
MAX_COILING_TEMP
Maximum temperature of coil at coiling
150-800 °C
MIN_COILING_TEMP_HEAD
Minimum temperature of head coil at coiling
150-800 °C
MAX_COILING_TEMP_HEAD
Maximum temperature of head coil at coiling
150-800°C
MAX_FORCE_STAND_(1-6)
Maximum Force at each stand (1-6)
500-3000 N
REDUCTION_STAND_(1-6)
Reduction at each stand (1-6)
5-700
Table 2.3-11 Main process variables at HSM and their admitted range The main variables were selected on the basis of process knowledge, because for a better use of statistical model is important to start directly with a reduced variables in numbers. The main variables are the temperature of coil at exit of finishing mill, at the final section of recoiling and for every stand the max value of applied force and the set reduction. The monitoring of these variables within their admitted range assure the standard of process. The aim of analysis was to find the relationships between process variables and the features of defect and to identify the main process variables that influence the defect. Starting from 6931 coils available, the sample was reduced to 4877 deleting out admitted range and missing records. The relationships between process variables and features of defect have been investigated by means of multivariate statistical analysis based on historical process data. Starting from the features of defect of “Salt &Pepper”, an important subclasses of scale defects, the defect index has been defined using PCA (Principal Component Analysis) to represent all the features of defect. In particular the index is the first component called “Prin1”. The Eigenvectors, reported in Table 2.3-12, show, the relative influence of defect features on the defect index (Prin1). The most important variables are the mean and standard deviation of longitudinal position of defects (highlighted on Table 2.3-13).
55
Eigenvectors Variables
Prin1
NUM_DEFECTS_B
-0.000028
AREA_MEAN_B
-0.002404
AREA_STDV_B
-0.006289
AREA_MAX_B
-0.067056
POS_X_MEAN_B
-0.00019
POS_X_STDV_B
0.000005
POS_Y_MEAN_B
0.648822
POS_Y_STDV_B
0.349758
NUM_DEFECTS_T
0.000331
AREA_MEAN_T
0.000493
AREA_STDV_T
0.002238
AREA_MAX_T
0.048774
POS_X_MEAN_T
-0.000012
POS_X_STDV_T
-0.000015
POS_Y_MEAN_T
0.567805
POS_Y_STDV_T
0.356889
Table 2.3-12 The Eigenvectors of Prin1 for Bottom and Top defect features By means of regression analysis, the defect index has been correlated with the process variables using the “stepwise” selection procedure usually provided in regression analysis. Table 2.3-13 shows the coefficients estimated (Parameter Estimate) for the selected process parameters and the relative influence (column Standardized Estimate) on the index estimated: the greatest values in absolute value correspond to the more determinant parameters.
56
Parameter
Standardized
Variable
Estimate
Estimate
Intercept
-3.41782
0
MAX_COILING_TEMP
0.00243
0.09620
MIN_COILING_TEMP
-0.00061753
-0.03355
AV_GAUGE_HE
0.09253
0.10502
AV_WIDTH_HE
-0.00070120
-0.09516
-0.00574
-0.13873
MAX_FORCE_STD_3
-0.00017894
-0.04822
MAX_FORCE_STD_4
0.00043636
0.10495
REDUCTION_STAND_1
0.03880
0.26320
REDUCTION_STAND_2
0.06359
0.31965
REDUCTION_STAND_3
0.02632
0.11106
REDUCTION_STAND_4
0.01316
0.04595
REDUCTION_STAND_5
0.04598
0.13619
REDUCTION_STAND_6
0.03605
0.10919
MAX_FM_TEMP_HE
Table 2.3-13 Coefficients estimated and influence degree of the parameters As shown in Table 2.3-13 the index estimated mainly depends on “REDUCTION_STAND_1” and “REDUCTION_STAND_2”. The good prediction model is shown both “adjusted R square” is high ( Adj R-Sq = 0.7476) and the observations are concentrated on main diagonal line (Figure 2.3-29).
Figure 2.3-29: Plot of first Component of ACP vs. prediction value The result obtained shows that there is a strong correlation between the features of Salt &Pepper defect (mainly the mean and standard deviation of longitudinal position of defects on top and bottom surface) and the process parameters (mainly the reduction at first two stands). D. Statistical Analysis of Check-list variables With the integration of check-list into DUFERCO data structure other important process variables are now available in suitable form. The Check-list is a list of manual measures performed on the lines that
57
describe the state of critical electro-mechanical parts of plant. The measures, in electronic way (off line and manually an operator inserts the data into database), are taken every start shift, in particular, daily at 8, 14 and 22. For these variables, CSM has carried out a statistical description to understand, in the dept, the technical status of plant and to compare them to the coil defects frequency. The variables of QTO Check-list (roughing mill section) are described, with target range defined into Practice Operative Standard on Table 2.3-15 In Table 2.3-16. At each variable is associated the colour according to the following meaning:
Green - the range is generally respected, Yellow – significant percentage of records is out of range . Red - the range is generally not respected Process parameters
Target range
Temperature of top-roll
30-70 C
Temperature of bottom-roll
30-70 C
Difference of temperature of rolls
5-10 C
Vertical stand movement
0 mm
Maximum value of movement
0 mm
Transfer table (electrical)
All
Scale presence after 3rd pass
No
Roughing passes
1-3-5-7
Descaling pressure of ramp-1
90-150 bar
% out of range
Descaling pressure of ramp-2
90-150 bar
Descaling pressure of roughing mill
up to 90 bar
Number of ramp used
2
Transfer table rotation (mechanical) Difference of Temperature of 2 pyrometers
5-12 C
>30%
Coil camber
No
Missing data
Alarm
2) and uncoated regions (vus>5). The wrong decisions increased but the critical wrong decisions were reduced to 1.3%. The confidence matrix is given in Figure 2.3-64
89
Figure 2.3-64: Result from the run with a modified decision tree on the reduced
Figure 2.3-65: Top: Result from the run with a modified decision tree on the complete data set (10620 coil sides). Bottom: Final confidence matrix after clearing of the result table The modified decision tree was then applied on the data of about half a year of production finally after clearing the set of 10620 coil sides. From this data set only 121 coils were predicted critical wrong
90
(Figure 2.3-65). All the 121 coils were examined again for inconsistencies. One difficulty was the fact that from a stopped coil the relevant side, top or bottom was not discernable and a decision to stop is valid for both sides although only one side fulfils the stopping conditions. Fortunately in all cases the defect types responsible for a stopping were given, so doubtful decisions mainly for the critically stopped coils could have been revised. As much as 101 coils were found, where both sides were marked as stopped by operator decision, while only one side was bad, which was correctly classified . The prediction of the bad side correctly was stop and to the good side correctly was release, despite the operators stop to the complete coil. At the end the wrong decisions could be reduced to 20 coil sides, which means down to 0.2%. All in all it seems to be possible to tune a mathematically optimised decision tree in a way that the critical decisions can be minimised. This can be done only with the disadvantage of decreasing overall accuracy. So if a figure from the production department could be given, to what extend critical decisions can be tolerated, it should be possible to tune a prediction system in a way that critical decisions are near that figure. Following the analysis done at ILVA about the nozzle stripe/streak defect at the Hot Dip Galvanizing Line, in Figure 2.3-66 it is pointed out that a simple threshold criterion (nozzle streak density > 0.09 m2 ) should be able to separate a certain quote of coils in which this defect is certainly present as cause of suspension. In the considered database, all the coils with Nozzle streak density greater than 0.09 m-2 have been suspended since they have been judged defective for nozzle stripe. Below this threshold, about 98 % of coils were ok or defective for other defects, referring with the decision of the Quality Department.
Nozzle_streak_133 0.088
Ok or suspended for other defect
(5/75) Suspended for nozzle stripe defect
Figure 2.3-66: Example of decision tree pertinent to those coils which are affected by the Nozzle streak defect in the Parsytec report As told in task 3.4, a Post-processing rule could be defined to improve the classification rate for the “Nozzle stripe” defect, to overcome the misclassification problems due to similar aspect defects (e.g. dark scratches and dirt). Once that Parsytec finds a Nozzle stripe defect, it could look backward for a selected group of aligned defects, among which Scratch dark, Dirt and Inclusions have to be certainly included, to finally identify the misclassified Nozzle stripe defects. If a reliable mapping of these defects on the surface could be done, it would be possible to have a suggestion about the best allocation for such coils: 2nd/3rd choice (depending on the area involved with the defect) or deviated to finishing lines for a precise and automated cut plan to recover the good part. Support on material allocation would increase line throughput, preventing from unnecessary material scrapping, even though only for coils whose cause of suspension is the Nozzle stripe defect. Having in mind these considerations and the knowledge acquired in the previous WPs, a realistic scheme of on-line advanced utilization of Parsytec data is given in Figure 2.3-67. If the ANN PrePost decision system (as ILVA developed for SPL and CAPL) is supposed to be coupled with a threshold
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criterion which acts only on a selected part of the processed coils (as ILVA proposed about Nozzle stripe affected coils in HDGL), a high transferability solution could be outlined. Considering the strict timing typical of a production line, the Pre-decision subsystem has not the chance of working really before the HLO, but at that very moment; in such schema, the HLO has to inspect all the coils, taking into account the Pre-decision allocation as a reminder, for example a green flag for surely non-defective coils. The Post-decision system works on the part of production deviated by the HLO and selects the surely defective coils. On that part of production, if the classification rate of a certain defect could be high, trimming or splitting actions could be scheduled for coils in automated way, preventing from unwanted downgrading or scrapping of good material. With such arrangement, QT department load decrease is achieved, and the increase of material recovery efficiency would lead to increased yield and reduced costs. Anyway, SURELY included here
Non defective
REMIND: Non defective
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To QT dept
n%
Parsytec DB
PRE-decisor
DB HLO (= all coils)
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The HLO inspects anyway all the coils. The PRE-decision classifier acts as “supervisor” of the HLO decisions.
Defective
Cut plan (trimming and/or splitting)
Defective for defect x1, xn
Defective for other defects
Figure 2.3-67: Realistic scheme of on-line advanced utilization of Parsytec data (ANN PrePost + Postprocessing) Until now AME was producing predictive models, in terms of cleaning capability of PL. For assessment purposes of carried out models oriented to improve decision strategy depending on surface quality, three different versions of these models have been developed, considering as a priority to be able for explaining users main reasons for results, which mean that at first level a tree reasoning based technology was required : 1. Complete model taking into account process parameters coming from the HSM and PL. This model can be run once the coil has been pickled and, for example, some scale is remaining and quality managers want to analyze the reasons for this fact. 2. Simplified model, taking into account mainly only parameters coming from the HSM. The only PL variable that is considered is the pickling speed. For this speed a high value, close to the maximum line speed, is assumed. This model can be used to predict, already at the exit of the HSM, if the coil is supposed to be correctly pickled under normal pickling conditions, even at a high speed.
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3. Simplified model, mainly with only parameters coming from the HSM. In this case a low value for the pickling speed, close to the speed used for problematic coils, is assumed. This model can be used to predict, already at the exit of the HSM, if it could be convenient to reduce the pickling speed to ensure the correct quality of the pickled coil. Next figure shows the percentage of coil surface with superficial defects on face 0 estimated after the hot rolling mill process (39 input variables), predicted against the real one, after the coil exits from the PL.
Figure 2.3-68 Cleaning capability predicted at HSM against the real one after PL Therefore a software tool was developed at AME for the application dependant presentation of the model output. This tool can be seen as well as support for advanced coil allocation as explained above as for control of the pickling line process, when it is used as a warning to the PL operator to reduce speed in case of a coil with poor predicted cleaning capability. Therefore the complete tool will be described in the next paragraph. 2.3.4.2
Task 4.2: Control of downstream process
The main focus of this work package was the integration of developed technologies. Some criteria regarding rules explaining high level of occurrence for defects were implemented as they are, trying to avoid these particular operating conditions with good agreement by technical people working in facilities. To integrate the output of the above three versions of the model for coil cleaning capabilities the following pieces of software needed to be created or modified: 1. Interfaces with the plant computers in order to be able to receive the information about the HSM process parameters as soon as the coil is rolled and about the pickled line parameters once the coil is pickled. This will allow to run the model and to present the results practically online. 2. Screens for the visualization of the model results 3. Modification of the screen used by the inspectors at the HSM for the online coil quality control, presenting the information about the predictions given by the model at this point. 4. Modification of the screen used by the quality managers for the offline analysis of quality problems, trying to give some hints about the origin of possible scale defects.
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Again, as these models are designed for running in real time, some change into the variable sources needs to be performed. Even when the meaning of variables are the same that in previous models (Table 2.3-17, Table 2.3-18 and Table 2.3-19), now an automatic and real-time source must be required. This is the reason why the name of variable‟ names changes, even when meaning is similar: Variable Description COL_GRL_TMP Coiling temperature FIM_CF6_TMP Temperature at the exit of the finishing mill FIM_TIJ_TMP Temperature at the entry of the finishing mill HOR_GRL_TIM_TOT Total time in the furnace REM_GRL_CNT_DESC Number of descalers used in the roughing mill REM_RMV_CNT Number of descalers used in the VSB REM_RMV_COD_DESC_VS Used of the VSB (`0`=NO, `1`=ENTRY, `2`=EXIT, B `3`=BOTH) COL_GRL_WTH_OBJ Final width FIM_GRL_THK_OBJ Final thickness FIM_CF0_THK_REDC Thickness reduction in first stand FIM_CF1_THK_REDC Thickness reduction in second stand FIM_CF2_THK_REDC Thickness reduction in third stand FIM_CF3_THK_REDC Thickness reduction in fourth stand FIM_CF4_THK_REDC Thickness reduction in fifth stand FIM_CF5_THK_REDC Thickness reduction in sixth stand Table 2.3-27 HSM line variables considered as relevant for real time prediction after HSM processing Variable Description TNQ_GRL_SPD_MN Minimal speed TNQ_GRL_SPD_MX Maximal speed TNQ_TN1_CON Acid concentration at tank 1 TNQ_TN1_TMP Temperature at tank 1 TNQ_TN2_CON Acid concentration at tank 2 TNQ_TN2_TMP Temperature at tank 2 TNQ_TN3_CON Acid concentration at tank 3 TNQ_TN3_TMP Temperature at tank 3 TNQ_TN4_CON Acid concentration at tank 4 TNQ_TN4_TMP Temperature at tank 4 Table 2.3-28 Pickling line variables considered as relevant for real time prediction after HSM processing Although the models are quite complex and not so easy to understand, it has been tried to present the model results to the user in a way as simple and clear as possible. Users are always interested in questions like which are the most influencing parameters and what should be done in order to solve the problem. Figure 2.3-69 shows the results of the complete model for a specific coil. The bar on the left represents the probability of having scale after pickling (56% in this case). Each of the lines gives information about the contribution of one parameters taking into account, grading the colour from green (parameters have a value that tends to avoid scale) to red (the values of the parameters are negative for the scale defect). For example, the text that appears in the first line has the following meaning: [SM01]FIM_TIJ_TMP = 1107.2 >= 1079.4 [SM01]: Parameter belongs to the HSM (the parameters from the pickling line start with “[DP02]” FIM_TIJ_TMP: Name of the specific parameter (temperature at the entry of the finishing mill in this case)
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1107.2: Real value of the considered parameter for this coil 1079.4: Threshold with which the real value is compared In order to keep the model results representation as clean as possible, only comparisons that have a positive answer are presented. In this way, operators know that one way to have a lower defect risk is to reduce the entry temperature under the limit proposed by the model.
Figure 2.3-69 Rules from complete version of the model used for practical implementation Following images show the results available as soon as the coil is rolled. As mentioned before, in this moment the real pickling conditions are not known and only the value for the pickling speed is supposed.
Figure 2.3-70 Model output when the pickling line speed is reduced for same coil shown in Figure 2.3-69. Previous image (Figure 2.3-70) shows the model results for the same coil when it is supposed a low pickling speed (100 m/min). It can be seen that the predicted defect risk (57%) is similar to the one calculated when all the real pickling conditions were known. Following image shows the model results assuming a higher pickling speed. Defect risk increased to 64%.
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Figure 2.3-71 Model output when the pickling line speed is increased for same coil shown in Figure 2.3-69 and Figure 2.3-70). For this specific coil, the reality was that some small scale defects were present after pickling. Model results can be considered correct in this case. Figure 2.3-72 shows how the model was integrated in the already existing coil quality tool. The three lines contain the information presented to the inspector in order to try to help him in the decision about the coil quality. Each column provides the information about one defect type. There is one line for the top side and another one for the bottom side. It can be seen that the columns corresponding to defects CPR (primary scale) and CSE (secondary scale) have been split into two for the top side. The areas with symbol “M” present the result of the model for this coil (existing models are only valid for these two defect classes and for the top side). The possible colours are: white (model predicts no problems), yellow (model predicts that speed needs to be reduced to guarantee the appropriate cleaning) and red (model predicts that scale cannot be removed even at a reduced speed).
Figure 2.3-72 Integration of developed models into the running coil quality tool. Clicking on the “M” symbol the following screen is open (see Figure 2.3-73). Here it can be seen that model predicts that even at a reduced pickling speed there is a high risk that the coil will not correctly cleaned. As explained before, this screen gives to the mill operations some online advises about which parameters should be modified in order to reduce this risk. In the previous image it can be seen in parallel the result of the existing grading system. The number of defects classified as primary scale (5) and secondary scale (17) is very low and is clearly under the allowed limits. That is the reason why the current system has not generated any alert for the inspector (white background). This coil could not be correctly pickled at the pickling line. In this case the results of the model are more accurate than the ones of the existing grading rules. This is one example of the limitations of these rules (based only on the ASIS results: defect number, severity and distribution) and how the new model can help to provide a better help to the inspectors.
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Figure 2.3-73 Detailed screen presented to mill operator after request showing risk evaluation (upper part when speed of the pickling line is reduced and lower part when it is increased).
2.3.4.3 2.3.4.3.1
Task 4.3: Optimisation of upstream process Maximize non critical scale level admissible in HSM
The model introduced at AME additionally can be used for the optimization of the hot strip mill process. Once the coil has been pickled with some remaining scale and the coil needs to be downgraded for this reason, it is necessary to analyze the causes of the insufficient cleaning in order to avoid the same problems in the future. With this objective following screen was prepared. It presents for the same coil the data from the HSM on the left side and from the PL on the right side. In this way defect maps can be compared (in this example it can be seen that part of the scale existing at the HSM has not been correctly cleaned after the PL). This screen integrates now the new model, as it can be seen in the following image. In this case the model presented is the one taking into account process parameter from the HSM and PL. This allows the quality experts to see what the model had predicted and the main reasons for this prediction. If the model results are correct, this can be a great help in order to discover which process parameter need to be change to try to avoid similar problems in the future.
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Figure 2.3-74 Reinforced learning by using post-mortem analysis is also considered and implemented 2.3.4.3.2
Development of system for robust cause-and-effect analysis
Also the developed solution for single defect mapping from hot strip mill to galvanizing line can be used in two different ways following the objective of upstream process optimisation. Figure 2.3-75 shows the approach of manual analysing single coils with quality problems and the possible reactions on the result whereas Figure 2.3-76 shows the approach of multi-coil analysis and automatic search for significant correlations between defect distributions of subsequent lines. Both approaches can lead to quality rules at HSM optimizing the HSM process. Automatic search for defect correlations in large coil-sets
Quality problem occurs
Check coil history
Check causes of GL and processing steps after HSM
No
Find significant amount of similar correlations Defect visible at HSM ?
Check HSM SIS Yes
Check HSM causes (process variables, in-order visualisation)
Apply cause-and-effect analysis for defect type
Create quality rule at HSM
Create quality rule at HSM
Figure 2.3-76 Multi coil analysis
Figure 2.3-75 Single coil analysis
In order to find suitable quality rules regarding single defect evolution from hot strip mill (HSM) to galvanizing line (GL) BFI investigated 5347 coils coming from AMEH regarding shell defect affection. Therefore the developed tool for automatic search of congruent defects was used. The coils were processed between March and July 2008 and all inspected by a Parsytec system at HSM and a Siemens VAI system at GL.
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First data evaluations showed that the amount of shell classifications at GL was 15 times higher than at the HSM. Besides the assumptions that some shells could have been inclusions at HSM and thus were not visible for the HSM ASIS and shells can be burst in smaller parts during cold rolling this difference seems to be not plausible and a start of the analysis at the GL shell classifications seemed not to be adequate. Therefore it was decided to use the following evaluation procedure for the first trial:
Every HSM coil is transformed to GL coordinates For every shell defect classified at HSM all defects within a search radius at GL were collected in a result database The search radius taken was defined based on previous manual investigations as 5cm cross- and 50m downweb direction.
The resulting dataset was evaluated by BFI and it stood out that just the heaviest shells detected by HSM ASIS had no shell defect at the associated GL inspection. It appeared that some of the HSM shells had unlikely large length in machine direction as shown in Figure 2.3-77. This turned out to be a quite common problem appearing from time to time at ASIS. Sometimes problems with the correct detection of the coil border can appear, leading to the detection of the border as heavy defects along the whole coil length. Surely these false detections of the coil border should not be considered for further evaluation. Another problem leading to the disappearance of heavy shell classifications is the confusion between shell defects and large slops of water occurring on the coil (Figure 2.3-78). Because water problems usually appeared periodically this problem could be filtered out by excluding all coils with number of water classifications above a certain threshold any thus just considering coils with low probability of water confusion. The last quite evident reason for non traceable heavy shells was that these severe defects were cut out after HSM to prevent the subsequent processing steps from damage. Thus these defects are not traceable through the production chain and also have to be excluded from further investigation.
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Figure 2.3-77: Example for border detection problem. Many false detections of the border are classified as shells with partly enormous length
Figure 2.3-78: Example of water detection classified as heavy shell at HSM ASIS This was managed by a further run of the search algorithm with an additional condition
Just consider coils of HSM with water detections below threshold
Just consider HSM shell defects with complete GL search area
For shell defects that were cut off during processing of the coil, gaps arise within the material tracking transformation, so vice versa a shell with complete context at GL was not cut out and can be used for further evaluation.
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The following figure shows the relative frequency of each defect class at GL within a search radius of a HSM shell compared to the total number of defects of the specific class at GL. The columns marked red show the shell type defect classes.
Figure 2.3-79: Relative class frequency of GL detections within HSM shell search radius The class with the maximum relative frequency consists of large aggregated defect areas. Therefore for this class less defects than usual exists which are all quite large and often overlap context areas of HSM shells. So it cannot be concluded that this kind of defect is significant often existing within these areas. On the other hand the maximum value of the two involved shell classes was just 2.1% meaning that 97.9% of specific shell classifications at GL where not within the searching area of a HSM shell. This results and the fact that the GL ASIS number of shell detections is much higher than at HSM suggests the conclusion that the GL ASIS classification has a low correctness, meaning that many defects classified as shell were no shell defects. Assuming that at least most of the real shell defects appearing at GL are correctly classified as shell defect (sureness) the following investigation was done to find rules concerning propagation and emergence of shell defects. Therefore a dataset was created that allows the formulation of a two class problem. The following classes were used for that investigation: Class 1: Class 2:
HSM shells with an existing GL shell within the search area (propagated) HSM shells without GL shell within the search area (non - propagated)
As process parameters the following were used for the investigation of the problem: Defect attributes (length, width, position, etc.) Coil attributes for GL coil (length, width, detection settings) Coil attributes for HSM coil (length, width, thickness, analysis, temperatures, rolling parameter) The compilation of the dataset showed that 6-times more HSM shell classifications were available for class 2 (non-propagated) than for class 1 (propagated). Therefore, already at the beginning, concerns on the assumption of a sufficient sureness for the shell classification at GL were apposite. Nevertheless the dataset was used to apply some data mining algorithms to find suitable correlations between the shell propagation and the available process parameter (Figure 2.3-80).
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Figure 2.3-80: Results of correlation analysis to find the main influences for the propagation of shell defects This figure shows a table displaying the five variables with the highest mean correlation coefficient over four different data mining methods and the component plane of the application of a self organizing map (SOM). The five variables are:
H_MAX_SEG max. horizontal segmentation region in pixel
V_MAX_SEG max. vertical segmentation region in pixel
MGVDIF
grayvalue difference segmentation-background
A
area of the defect (mm2)
SIZEMD
length in machine direction (mm)
Despite MGVDIV describing the contrast of the defect, all parameters found are related to the size of the defect. To find rules for the propagation of surface defect additionally a decision tree was trained. Figure 2.3-81 shows the two most significant rules generated. It has to be stated that these results have to be interpreted very carefully because of the weak classification performance of shell defects at GL. Nevertheless the presented approach of finding relevant rules for the propagation of defects can be seen as best-practice procedure and can be easily repeated with new data of enhanced classification accuracy.
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Figure 2.3-81: Best decision tree rules for the shell propagation. First rule for non-propagated, second rule for propagated shells 2.3.4.3.2.1 Study of cause of defect shell (detailed description of this analysis was done into DLL+CSM contribution report n. 5) DUFERCO carried on a analysis on the causes of shell defect in order to understand the emerging condition of defect and the reason of non correlation from the statistical analysis with the casting condition. The correlation analysis was performed into three step. The first step was the chemical analysis of defect where 23 samples were used. The typical defect analysed were different kind of shell (closed, open, lengthen)
with dust presence (50% of samples)
with iron oxide (50% of samples)
Figure 2.3-82: Chemical analysis of different defect with presence of dust or iron oxide From the chemical analysis 50 % of shell depends on the melting phase while the other 50% have different causes. The second step was the study of localization of defect into coil (slab). The analysis was made with slab of different supplier (CARSID, COBRA, TEESSIDE, NMLK) with the comparison of the results of Inspection System
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Frequency of shell's positions on slab's length 2008 Carsid/ Slab's bottom surface
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Figure 2.3-83: Defect distribution on slab surface for CARSID supplier
Frequency of shell's positions on slab's length 2008 NMLK/ Slab's bottom surface
Frequency
20 15 10 5 0 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 Position on slab's lenght (m) Figure 2.3-84: Defect distribution on slab surface for NMLK supplier From the defects distribution diagram the following : 1. The shells are main in the lower side of slab (more than 90%) especially for two slab supplier (CARSID in Figure 2.3-83 and COBRA). 2. 50% of chemical shell analysis have the presence of dust; the hypothesis is the presence of oxycutting cordon that is rolled during the roughing mill process. 3. The slab supplier Teesside and NMLK usually remove the oxycutting cordon 4. The main localization of defect in coil surface are the extremes (head and tail) for slab from CARSID (Figure 2.3-83) and COBRA, and no preferential localization for TEESSIDE and NMLK slabs (Figure 2.3-84) The third step is the validation of the hypothesis of the oxycutting cordon presence. For this validation test DUFERCO was removed the oxycutting cordon from 197 Carsid slab and the defect distribution is
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compared with the slab where the same cordon was not removed. The results, shown in the Figure 2.3-85, shown that the distribution of defects decreases in number and localization. Frequency of position of shell on the slab's length- test of removing the burr w ith burr :270 coils
w ithout burr:197 coils
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Figure 2.3-85: Defect distribution comparison between slab with cordon and without The main results of this activity is the individuation of an important causes of shell defect (presence of oxycutting cordon on slab). This element is missing usually from the process variables and so the automatic correlation between defect and process is very difficult. 2.3.4.3.2.2 Study of cause of defect scale (detailed description of this analysis was done into DLL+CSM contribution report n. 5) By the frequency distribution analysis an important correlation was found with the use of coil-box with particular steel grade (presence of phosphorus into chemical analysis) and DUFERCO carried on an analysis also for the causes of defect scale. From its BEATOR customer a lots of claims for scale defect of particular steel grade (high presence of phosphorus in chemical analysis) In order to investigate the frequency of that claims all the different classes of scale are gathered together in one single macro class named SCALE. Then the coils were classified with a new variable QUALITY (0/1) on the basis of the number of defects SCALE (number > 250 the QUALITY is 0=Bad, otherwise 1=Good) The fist Histogram analysis (Figure 2.3-86) confirms the customer hypothesis about the more defectiveness of steel with high presence of phosphorus
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Figure 2.3-86: Coil distribution (QUALITY=0 red , QUALITY=1 green) The hypothesis of cause of scale defect is the using of coil-box during the rolling process. This hypothesis is formulated by the technical background of DUFERCO and the histograms of Figure 2.3-87 and Figure 2.3-88 confirm it. In fact the coil defectiveness for the same steel grade increases with the using of Coil Box. (from a percentage of only 13% to 64% )
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Figure 2.3-87: distribution for steel with phosphorous and Coil Box using
Figure 2.3-88: Coil distribution for steel with phosphorus presence and without Coil Box using
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2.3.5
WP5: Assessment
The last work package of the project was devoted to the assessment of the developed solutions. 2.3.5.1 Task 5.1: Test of functionality and system tuning The developed applications were extensively tested in operational practice and adapted to the needs of the production process. The test of the coil allocation with the decision tree as set up in the previous section was performed at data from actual production, about 1600 coil sides. An operator decision for release or stopping was collected from the manual recordings. From the condensed coil inspection data and the manual recorded data a test set was combined. Again the difficulty came up, that from a stopped coil the relevant side, top or bottom was not discernable and a decision to stop is valid for both sides although only one side fulfils the stopping conditions. But as well as in the situation described above the defect types responsible for a stopping could be identified and be used to decide, which side was the reason for stopping. In Figure 2.3-89 the results before and after corrections are given.
Figure 2.3-89: Result from the run with a data set from current production (1600 coil sides), left: corrected, right: uncorrected Again the number of critical decisions is very small at cost of the overall accuracy. Before correction as described above there were more wrong decisions (53.3%) as correct ones (46.7%), fortunately with a small value of critical decisions (1.6%). This image is improved after correction to 48.2% correct, 51.8% wrong and 0.1% critical decisions. From this research it turned out, that despite the 0.1% critical decisions the overall accuracy of the models is not good enough, though even in this state it can be applied useful in production. The recording and relation of inspection results not only to a single coil but to the relevant side of the coil, the clear reason for stopping and other information, necessary for consistent data sets useful for model training have to be improved to come closer to that aim. Also the accuracy of the inspection system in view of detection and even more of classification has to come to a higher level. It turned out that the main features, leading to a stop of the coil are the individual numbers of severe defects, the inspection sensitivity and the number periodic defects. Only to a small extend the geometrical information about streaks, blobs or defect densities influences the coil release. But nevertheless if a more differentiated coil allocation has to be performed this Information can be very useful. A similar tuning towards a smaller amount of critical false decisions was investigated for the ANN trained by ILVA trying to overcome the drawback related to the misclassified coils at the Pre-decision subsystem at CAPL. Therefore the threshold of the ANN has been adjusted, but results put on evidence some kind of instability: the percentage of defective coils wrongly accepted as “ok” by the system is weakly influenced by the threshold; moreover, below the threshold value 0.05, the pre-decision system
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becomes unstable and accepts a small fraction of coils as surely non-defective (0.13%), with a very high part of wrongly accepted (28.6% are actually defective).
Performance vs. Threshold 100%
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Figure 2.3-90: Pre-decision system behaviour depending on the threshold setting (blue line: coils selected by Pre as surely non-defective, scale on the left. Green line: defective wrongly accepted within this class, scale on the right) Deep analysis has been dedicated to the comprehension of this phenomenon, and it was pointed out that about 40% of misallocated at PRE do not referred to surface defects, due to some difficulty occurred during data preparation put on evidence only during the last part of the project. Most of the remaining misallocated coils had been deviated to the finishing lines due to surface defects, but localized in a very specific part of the coil (e.g. head or tail). So the Pre-decision subsystem gave a good general grading to such coils, but HLO deviated them to the finishing lines to eliminate the bad part and recover the good part (the most). Finally, the defective coils really accepted as “ok” by the Pre decision subsystem should have been reasonably below 1%. At AME the developed model has been implemented in the plant and the results were followed for several months, although for the moment it is still not used for taking the decision about the coil quality or controlling the speed at the pickling line. Figure 2.3-91 tries to summarize the results. The green point represents the current situation. It can be seen that the performances of the used criteria are really very poor. More than half of the coils that are downgraded at the HSM because of scale defects are correctly cleaned after pickling. And on the other hand, only about 10% of coils with scale after pickling are correctly predicted at the hot strip mill. The ROC curve built with validation data shows that, although the model is not perfect, it is able to improve clearly the current situation. The red point represents the values for a big number of coils processed under completely normal production conditions. The point lies exactly on the ROC curve, which means that the model is robust and its performances do not decrease when used in an industrial way. The brown point represents the results of the model applied at the exit of the HSM supposing a maximal speed at the pickling line. In this case we adopt a secure position, asking more often for a reduction of the speed at the pickling line. It can be seen that, if this recommendations were followed, more that 80% of the coils with scale after pickling could be avoided. The negative part is that this speed reduction would not be necessary in about 40% of the cases. The point for this model lies under the ROC curve. This is normal, as the model is applied at the exit of the HSM and in this moment not all the parameter are known. .
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1 0.9 0.8
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Figure 2.3-91 ROC curve of the Advanced Decision Tree model implemented in the plant 2.3.5.2 Task 5.2: Determination of cost/profit relation For a comparable assessment as well for the cost/profit relation as for the transferability of the developed solutions a survey was invented. This survey was send to each partner and filled for any developed solution. In the following paragraphs about task 5.2 and 5.3 a short summary about the result for the involved lines should be given. For a detailed overview on each single solution please refer to Annex 5.2. It must be stated that most of the profit calculation can just be tentative, since the actual assessment depends on many unknown parameters, which are not foreseeable at now. 2.3.5.2.1
Hot strip mill
The analysis of cost/profit relation was made for the main objectives reached by project, the check-list implementation, the quality data integration and the application of diagnosis rules for quality management . 2.3.5.2.1.1 Check-List The check-list is a very important result of this project. With the definition of check-list new information about the process status is available for Quality staff. The main problem to this approach is the organization of all documents and the involvement of a great numbers of persons for a long period from different plant department (Process, Maintenance, Quality). About the costs the solution required man hours during the preliminary organization of new variables and the presence of dedicated personnel during every shift hours that perform the inspection tour with the data collection. Another important cost of this approach could be the adoption of electronic devices to support the operations of data gathering and storage. This support could be realized with personal “palm” or touch-screen terminal. The total cost was estimated about 35 k€ (25 k€/year of manhours and 10 k€ for hw/sw tools). The benefits evaluation is not easy for the implementation of this approach. That is because the immediate benefits of this solution mainly address the maintenance aspect and consequently other plant staff. The cost quantification could be done on the basis of consideration about the plan maintenance. The daily monitoring of status of mechanical parts of process line improve in any case the global management of process.
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For the surface quality the benefit is the reduction of reaction time in case of plant's problems and consequently less quality loss. 2.3.5.2.1.2 Data Integration The results of this project confirm the fundamental availability of integrated database for quality use where all information is linked together. For a wide overall of product quality the process variables, the customer data, the ASIS results and the product final use description have to be easily and quickly accessible. To reach this objective is mandatory the presence of a product tracking and for the process line, the presence of data communication with level 2 of automation. The implementation of this approach provides the installation of one dedicated workstation as data archive and the software development of procedures for data acquisition and displaying for the Quality operator. The total cost was estimated about 35 k€. The immediate benefit is estimated on the reduction of time for Quality staff spent to gather and organize data to help the decision on defect presents of coil. The benefit time reduction is estimated about 10 k€ per year. 2.3.5.2.1.3 Quality management (cause effect correlation) The minimal cost for the implementation of a quality philosophy is devoted to data-mining software, and the quality staff training to daily use of these tools. The total cost was estimated about 45 k€. There are two main benefits with the cause-effect correlation: one is related to the reduction of time, the time to make analysis and find solution reduction of time to make analysis and find solution The other benefit is related to the action related to the solution applied for the reduction of the downgrading material. DLL has estimated that the downgrading of material for defect shell is about 2,3 M€ per year. In case of transferability of this solution the benefits will be related to the actions defined for new application. On the basis of the results of correlation analysis of defect shell on coils, as described into activity chapter, the DUFERCO transmitted to slab supplier the right actions in order to remove the defect causes. The elimination of this defect cause involve about 40% of slab and, on the basis of analysis of coil grading management with presence of defect, the pay back could be about 2 million euro for year. The global investment to apply these solutions is estimated around 100 k€ It‟s remarkable the fact that the general costs of all solution are related to software tools and man hours while the benefits on the application of solution can be huge in terms of materials quality management because the feedback decision is taken at beginning of process chain (Hot Strip Mill) and involve all material. 2.3.5.2.1.4 Pickling line For the calculation of cost/profit, following values were assumed: Pickling line production: 960000 t/y Cost of coil reparation at the HSM: 17 €/t Cost of coil downgrading at the HSM: 75 €/t Mean cost for coil downgrading/reparation at the HSM: 46 €/t Mean cost for coil downgrading/reparation at the PL: 58 €/t Cost of reduced speed at the PL: 1000 €/h The total benefit of the model sums up to 269 k€/year resulting in an amortization time of less than one month of the developed solution (refer to Annex 5.2 for further details).
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2.3.5.2.2 Final production lines Coil allocation at final production lines uses not only information from surface inspection but many different quality information like roughness values or processing parameters like temperatures. So a coil allocation by surface inspection is only a part of the decision process leading to the final decision about the coil. In a first trial the method for coil allocation developed during this project is applied offline for the inspection results of the HDG2 in Salzgitter. The results are transferred to the production department as a list of coils from the last day being released, in doubt or stopped. In this way the feedback of the operators can be get and influences additional tuning of the accuracy. By using the developed tools the following calculation can show the benefit for the production. A HDG produces depending on the actual situation different quality grades. A common situation is a percentage of 20% lowest quality grade, 60% medium and 20% highest quality. A production of about 20000 coils per year would result in 12000 medium and 4000 high quality coils. If only the medium and high quality coils are regarded, with 50% correct decisions only the coils stopped by automated allocation have to be controlled, approximately 2000 coils for high quality and 6000 coils for medium quality. If the operator relies only on automated surface inspection, the control of a coil defect by defect would last about 10 minutes. So at one production line a time benefit of about 1300h, about two thirds of a year working time for one person can be gained. The monetary benefit depends on the cost per hour. Additionally the PrePost decision system developed at ILVA for the Continuous Annealing Process Line (CAPL) has been selected for an outlined treatment about transferability and cost/profit issues. As can be seen in Annex 5.2 the total benefit of the solution sums up to 90 k€/year resulting in an amortization time of less than one an a half years of the developed solution. 2.3.5.3 Task 5.3: Determination of transferability The developed solutions were analysed concerning transferability to other plants by specification of the prerequisites for successful operation. 2.3.5.3.1 Advanced coil allocation The software developed and the database structure uses only data that will be delivered from any modern surface inspection, mainly classified defect type, geometrical defect data, inspection sensitivity and coil information being available also for the operator. Due to this structure the system can be fitted easily to other production lines. Most important precondition is a system with a high detection and classification rate. The core of the allocation system, namely the decision tree or other classification algorithm has to be trained at local data with support of manual decisions from operators. For a statistic large enough for consistent results about 5000 coils have to be collected and allocated by operators, this would take a time between 3 month and half a year. 2.3.5.3.2
Control of downstream process
Following image (see Figure 2.3-92) tries to show the way in which the decision about the coil quality is taken currently at the exit of the HSM. The only available information is the one obtained from the ASIS (defect data and images). The problem with this approach is that it is not always easy only by looking at the coil map and the defect images at the HSM to predict if scale will be removed after the PL (mainly due to the lack of information about defect depth).
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Figure 2.3-92 Previous decision making model regarding quality of coils. The solution implemented in this project consists in adding some information about process parameters in order to know in which conditions defect was produced and by the using of the adequate models to try to predict how easily scale can be removed (see Figure 2.3-93).
Figure 2.3-93 New decision making model regarding quality of coils. Concerning transferability, the two main prerequisites for a successful operation are: 1. ASIS at HSM and PL with a minimum performance level concerning detection and classification 2. It must be taken into account that it is a statistical model, which means that it needs to be readapted for the new plant. For a physical model adaptations are normally easier, as it can be enough to give the plant specific values to some configuration parameters. 2.3.5.3.3 Optimisation of upstream process The transferability of the system is guaranteed by the presence of non-critical elements in establishment. The solutions adopted are online on application procedures for acquisition and synchronizing data with the adoption of a system of acquisition manual for the detection of not measurable situations related to parts of plant which contribute to the quality of the material. For the complete transferability of the tools developed by the project the following requirements must be fit: Tracking material presence Available of process data on structured DB Skilled personnel for data analysis Very important aspect for the transferability of the solution is the reliability of ASIS data and in particular of classification performance. That is the critical point because the application of correlation rules starts for the automatic evaluation of defect presence on coils. In order to monitor and assure the ASIS reliability is necessary to provide a maintenance plan. This plan contains operation for the system cleanness, to avoid pollution of water and dust on the optic parts in order to assure the defect detection, and operation for defect classification, monitoring in order to prepare the classifier training when necessary (appearance of new defect classes)
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These demands are very critical because long time is required to operators and investments could be done on special software tools to help the Quality staff. In order to optimize these aspects it is mandatory to develop an optimized strategy as results of research of best practices.
2.4
Conclusions
The activities planned for this project are mainly reached during the project and the achieved results are satisfactory. The conclusions gathered during the execution of this project can be summarized as follows:
Today many systems are simply used as coil browser without any data usage
The consolidation of verified ASIS and process data usable for cause-and-effect analysis is a very time consuming task
The main prerequisite for the improved data utilisation of ASIS data is a sufficient detection and classification performance of the system ensuring an adequate reliability of the data.
Undefined procedures for the general assessment of ASIS data and missing monitoring solutions complicate the gathering of reliable data required to build suitable models
ASIS are no measuring devices and the generated data is not easily comparable as their formal precision and accuracy is undefined
First step of data utilisation should be an application adapted post-processing for filtering, verification and aggregation of ASIS mass data
ASIS data of HSM can be used to increase line throughput of the pickling line
By using the results of ASIS for cause and effect analysis the final product quality can be improved. This could be shown for shell investigation (burr) and scale analysis for high phosphate steel grades (coil box) for the HSM process.
By using ASIS data combined with gapless material tracking, rules for the propagation and emergence of surface defects can be generated. This could be shown as well for area defects (scale) as for single defects (shell)
By using ASIS data to quantify the scale affection at HSM the amount of downgraded coils can be reduced. This results in increased yield at finishing lines
By using the results of ASIS for cause and effect analysis and generation of suitable models, rules for process optimization can be derived
The utilisation of ASIS data for cause-and-effect analysis of sticker defects at HDGL lead to less downgraded material at finishing lines
“Number of defects” revealed to be not enough to explain coil grading task
The coil allocation task is very difficult to realize, because the operator decision includes many additional information not covered by ASIS data and not even measured by other devices.
The integration of ASIS results and process data permits quality staff to perform better coil grading.
The implementation of a check list procedure can help to monitor quality relevant process parameter currently not automatically recorded by measuring systems. This approach could be improved by implementation of an automatic gathering procedure.
Models for advanced coil allocation should be optimized to prevent critical misclassifications (release of coils that should be stopped). This is easier for decision tree models than for ANNs.
Making use of the potential within ASIS data is complex but also worthwhile
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2.5
Exploitation and impact of the research results
Most important results of the research project for SALZGITTER are at first the set up of a common database with a data structure, usable for all inspection system in Salzgitter. This database is meanwhile used from different departments for cause effect analysis, statistical and data mining examinations. A second important result was the development of software for condensing defect maps. This software was implemented in the data collection software for the data-base. The results are presented to the operators for easier quality decisions and are meanwhile accepted from the production department. Presently the software is used to get experience and define the direction in which a further development has to go. At DUFERCO the results of project are now applied to the Hot Strip plant but the methods will be transferred to pickling line in order to have a complete vision of defect surface quality of product for DLL. At AME the developed model has been implemented in the plant and the results were followed for several months, although for the moment it is still not used for taking the decision about the coil quality or controlling the speed at the pickling line. The benefits of the developed model can be summarized in the following points: 1. Earlier coil downgrading in cases where models predicts that the scale removal will be impossible: o
The costs of the coil processing at the PL are avoided.
o
Easier coil reallocation at the HSM, as it can be sold as black coil.
o
Others: faster response to substitute the downgrading coils (OTIF objectives)
2. Avoiding of incorrect downgrading at the HSM in cases where model predicts the coils can be correctly pickled. 3. Avoiding of coil downgrading after pickling in cases where model predicts that coils can be correctly pickled at a reduced speed. In this case, the most important value for the calculation is the cost difference between coil downgrading and loss of productivity due to the reduced speed. 4. Increase of productivity in cases where model predicts that coils can be correctly pickled even at a higher speed At ILVA the defined routines to get reliable ASIS data are currently in use, and the correlation analysis between ASIS/process/quality is an everyday growing practice, even if still on discontinuous base. Though at now an automatic coil classification based on automatic surface inspection is not favourably seen by the managing staff, it allowed pointing out advantages and drawbacks of such a technique, and the limitations imposed by current IT arrangement. The experience about the ASIS systems gained in the IRSIS project has been shared among quality and metallurgy staff, so that Parsytec usage is becoming a more analytical activity than in the past, and the defects tracking along the process route (from HSM ILVA Taranto to HDGL ILVA Novi Ligure) is now seen as an interesting and achievable goal. BFI developed a post-processing framework that finally could be brought to the market as the first solution working on any kind of surface inspection data. By now it is possible to process the data of all surface inspection systems of one facility with the same rule set without depending on the ASIS supplier and convert the mass data produced by ASIS to useful information for further application. The solution developed for single defect mapping was implemented at ARCELORMITTAL EISENHÜTTENSTADT and the 3 use cases described in task 2.4 were realized. The manual tracking application is currently used by the quality department to handle complaints. The side-by-side visualisation of the HSM and GL inspections of the same coil is a valuable tool for this task saving a lot of time for manual data acquisition. Additionally the first trials using the automatic search algorithms caused the start of a new campaign improving the classification of GL inspection system and motivated further research activities on the investigation of defect evolution.
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Regarding the technical and economic potential for the use of the results please refer to Annex 5.1 2.5.1
Publications of gathered knowledge.
In addition to previous exploitation activities, two research papers were sent for publication to first level scientific journals:
An advanced predictive system for the cleaning of steel coils using artificial intelligence technology. submitted to IEEE Intelligent Systems
A global quality strategy for steel strip production using advanced industrial informatics. Submitted to IEEE Transactions on Industrial Informatics.
Including inside appropriate references and acknowledgements to the RFCS research programme. No possible patent filing.
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3 3.1
List of figures and tables List of figures
Figure 2.3-1 Class distribution at SALZGITTER HDG 1....................................................................... 16 Figure 2.3-2 Distribution of defect width (top) and length (bottom) ...................................................... 16 Figure 2.3-3 Schematic representation of coil allocation procedure ....................................................... 18 Figure 2.3-4 Tracking sample of a coil ................................................................................................... 20 Figure 2.3-5: First classifier tool proposal at ILVA, not applied because not adaptable to actually available data ........................................................................................................................................... 22 Figure 2.3-6: Schema of generic artificial neural network ...................................................................... 22 Figure 2.3-8: Integration of data structures for the collection Inspection data from the galvanising lines. The ASIS application evaluates the inspection data by condensing and extraction of key figures (Green: Existing data structures; Red: Tested but not established structures) ..................................................... 25 Figure 2.3-9 Concept for application of the BFI tool for post processing............................................... 25 Figure 2.3-10 Mapping of coil/defect-attributes to database columns .................................................... 26 Figure 2.3-11: Schematic representation of data integration at ILVA .................................................... 28 Figure 2.3-12 General architecture of data integration developed .......................................................... 30 Figure 2.3-13 Main panel of data gathering and extraction software tool............................................... 30 Figure 2.3-14: Databases collected at ILVA ........................................................................................... 31 Figure 2.3-15 Defect density approach based on Gaussian diffusive concept for comparing coil maps. 37 Figure 2.3-16: Theoretical curve (green line: non defective coils. Blue line: defective coils) in case of the number of defects were the main parameter to take into account during coil allocation .................. 37 Figure 2.3-17: Qualitative real curve (green line: non defective coils. Blue line: defective coils), in which the number of the defects is not strictly related to the defective or non-defective status of a coil 37 Figure 2.3-18: Definition of five coil zones ............................................................................................ 39 Figure 2.3-19.- Main pictures for one particular coil showing scale defects at the exit of the HSM (left pictures) and after pickling line (right) and for the upper face (first row pictures) as well as for the opposite face (last row pictures). Color points represent different types of scale defects. ...................... 41 Figure 2.3-20 Image showing how new unforeseen defects are identified by pickling line (upper right corner). .................................................................................................................................................... 42 Figure 2.3-21: Concept of single defect mapping solution ..................................................................... 42 Figure 2.3-23: Example evaluation of context rule: “If more than 2 triangles in context, then also triangle” (context represented as dashed square) ................................................................................... 45 Figure 2.3-24: Automated transfer and post processing of the ASIS data, detailed Software diagram for the ASIS application given in Figure 2.3-8 ............................................................................................. 47 Figure 2.3-25: Post-Processing result for defect aggregation (10 m downweb, 10 cm crossweb) .......... 47 Figure 2.3-26: Typical defect map of two different coils; left – right: strip width; top – bottom: strip length Red boxes: Severe defects Green boxes: Y-stripes, Yellow boxes: X-stripes Blue boxes: Blobs................................................................................................................................. 48 Figure 2.3-27 Schematic approach of preliminary analysis for data preparation .................................... 49 Figure 2.3-28 Schematic approach of data collection and preparation ................................................... 50 Figure 2.3-29: Plot of first Component of ACP vs. prediction value ...................................................... 57
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Figure 2.3-30: Distribution of the value of Descaling pressure of ramp-1 on first period ..................... 59 Figure 2.3-31: Distribution of the value of Descaling pressure of ramp-1 on second period ................ 59 Figure 2.3-32 SVM results: cleaning variable (solid blue line), SVM output with 38 input variables (dashed red line). ..................................................................................................................................... 62 Figure 2.3-33 Visual summary of quality factors for developed predicting model ................................. 64 Figure 2.3-34 Sensitivity of PL against variation of its speed. Patterns ranked for descending predicted cleaning capability. .................................................................................................................................. 65 Figure 2.3-35 Defect surface of coil with defects in its first 100m, according to different steel grades. 65 Figure 2.3-36 J48 tree explaining high density of defects according to different variables relevant for one particular steel grade. ........................................................................................................................ 66 Figure 2.3-37: Stickers on cold rolled strip ............................................................................................. 67 Figure 2.3-38: Example for a decision tree for the prediction of stickers. For the parameters see Table 2.3-23. One important parameter is “UNFLATRATIO”, the flatness of the strip at the tandem mill. This parameter was not taken into account seriously up to the results of the examination. Other important parameters are coiling tension and annealing temperature, agreeing to the experience of process experts ................................................................................................................................................................. 69 Figure 2.3-39: Suspended coils per steel grade (different steel grades indicated with different letters) . 70 Figure 2.3-40: Defects density for the major 10 clients in the reference period ..................................... 71 Figure 2.3-41: Mean defects density per choice of coil allocation by QT, within suspended production ................................................................................................................................................................. 71 Figure 2.3-42: Daily mean zinc bath defect density per coil (and Al % in Zn bath) ............................... 72 Figure 2.3-43: The Zinc Streak defect, as seen by the cameras of the Parsytec installed at the HDGL . 73 Figure 2.3-44: The Zinc Streak defect density coil by coil depending on the process speed (different colours mean different thickness, as in the key on the top of the diagram) ............................................ 73 Figure 2.3-45: The same data of Figure 2.3-44, arranged per process speed classes .............................. 74 Figure 2.3-46: Defects which show a relevant correlation with the process speed ................................. 74 Figure 2.3-47: Defects which show a relevant correlation with the coating thickness (measured as upper coating) .................................................................................................................................................... 75 Figure 2.3-48: (left) and 16 (right): Nozzle stripe (left) and Nozzle streak (right) defects as seen by Parsytec cameras ..................................................................................................................................... 75 Figure 2.3-49: Correlation between Nozzle streak Parsytec occurrence (expressed in n/m2) and the coils actually judged as defective for nozzle stripe defect by the Quality Department ................................... 76 Figure 2.3-50: Same data of Figure 2.3-49, arranged per nozzle streak density class ............................ 76 Figure 2.3-51 Example of mapped shell defect visible at HSM bottom side (middle) and GL top (left) and bottom side (right) ............................................................................................................................ 77 Figure 2.3-52 Use of post-processing algorithms for automatic defect associations .............................. 78 Figure 2.3-53 Evolution of incoherence per month in a real production set. .......................................... 79 Figure 2.3-54: current situation, without automatic coil grading or allocation. 6% of coils of the whole SPL database has been sent to quality department .................................................................................. 82 Figure 2.3-55: using a neural network pre and post decision makers could lead to a great reduction of the amount of coils sent to quality department (about 3.4% of the whole SPL database). 7.5% of DB HLO deviated by HLO corresponds to 5% of SPL DB (difference between this value and previous 6% due to the error of the pre-decisor, i.e. misallocated coils by the automatic system) .............................. 82 Figure 2.3-56: Simulation of ANN on 2006 CAPL DB, scheme and data.............................................. 83
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Figure 2.3-57: Detail table of post processing id: Index; ts: Production date; bandnr: Coil ID; seite: Top / bottom side grp: Number of collection; typ: Type of collection; mdp..wlo: defect keys; xs, ys: Startposition; xl, yl: length; Fl_Box: Area; d_per: period length ............................................................ 83 Figure 2.3-58: Steps to automated coil allocation by a trained prediction model ................................... 84 Figure 2.3-59: Classification scheme for severe defects (red boxes), : Number of severe defects ......... 85 Figure 2.3-60: Summary table of post processing for use in model training .......................................... 86 Figure 2.3-61: KNIME workflow scheme, functions described in text above ........................................ 87 Figure 2.3-62: Result from the first run with a decision tree, TK: Operator, Pred: prediction, Frei: released coil, Gesp: stopped coil ............................................................................................................. 87 Figure 2.3-63: Cross validation workflow Upper: overview Centre: Detailed workflow, function described in text above Lower: Result of Cross validation ................................................................. 89 Figure 2.3-64: Result from the run with a modified decision tree on the reduced .................................. 90 Figure 2.3-65: Top: Result from the run with a modified decision tree on the complete data set (10620 coil sides). Bottom: Final confidence matrix after clearing of the result table........................................ 90 Figure 2.3-66: Example of decision tree pertinent to those coils which are affected by the Nozzle streak defect in the Parsytec report .................................................................................................................... 91 Figure 2.3-67: Realistic scheme of on-line advanced utilization of Parsytec data (ANN PrePost + Postprocessing) .............................................................................................................................................. 92 Figure 2.3-68 Cleaning capability predicted at HSM against the real one after PL ................................ 93 Figure 2.3-69 Rules from complete version of the model used for practical implementation ................ 95 Figure 2.3-70 Model output when the pickling line speed is reduced for same coil shown in Figure 2.3-69....................................................................................................................................................... 95 Figure 2.3-71 Model output when the pickling line speed is increased for same coil shown in Figure 2.3-69 and Figure 2.3-70). ....................................................................................................................... 96 Figure 2.3-72 Integration of developed models into the running coil quality tool. ................................. 96 Figure 2.3-73 Detailed screen presented to mill operator after request showing risk evaluation (upper part when speed of the pickling line is reduced and lower part when it is increased)............................. 97 Figure 2.3-74 Reinforced learning by using post-mortem analysis is also considered and implemented98 Figure 2.3-75 Single coil analysis ........................................................................................................... 98 Figure 2.3-76 Multi coil analysis ............................................................................................................ 98 Figure 2.3-77: Example for border detection problem. Many false detections of the border are classified as shells with partly enormous length ................................................................................................... 100 Figure 2.3-78: Example of water detection classified as heavy shell at HSM ASIS ............................. 100 Figure 2.3-79: Relative class frequency of GL detections within HSM shell search radius ................. 101 Figure 2.3-80: Results of correlation analysis to find the main influences for the propagation of shell defects.................................................................................................................................................... 102 Figure 2.3-81: Best decision tree rules for the shell propagation. First rule for non-propagated, second rule for propagated shells ...................................................................................................................... 103 Figure 2.3-82: Chemical analysis of different defect with presence of dust or iron oxide .................... 103 Figure 2.3-83: Defect distribution on slab surface for CARSID supplier ............................................ 104 Figure 2.3-84: Defect distribution on slab surface for NMLK supplier ................................................ 104 Figure 2.3-85: Defect distribution comparison between slab with cordon and without ........................ 105 Figure 2.3-86: Coil distribution (QUALITY=0 red , QUALITY=1 green) .......................................... 106
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Figure 2.3-87: distribution for steel with phosphorous and Coil Box using ......................................... 107 Figure 2.3-88: Coil distribution for steel with phosphorus presence and without Coil Box using ....... 107 Figure 2.3-89: Result from the run with a data set from current production (1600 coil sides), left: corrected, right: uncorrected.................................................................................................................. 108 Figure 2.3-90: Pre-decision system behaviour depending on the threshold setting (blue line: coils selected by Pre as surely non-defective, scale on the left. Green line: defective wrongly accepted within this class, scale on the right) .................................................................................................................. 109 Figure 2.3-91 ROC curve of the Advanced Decision Tree model implemented in the plant ................ 110 Figure 2.3-92 Previous decision making model regarding quality of coils. .......................................... 113 Figure 2.3-93 New decision making model regarding quality of coils. ................................................ 113
3.2
List of tables
Table 3.3-1: Systems involved in this project ....................................................................................... 14 Table 3.3-2: Summary of ASIS states ................................................................................................... 15 Table 3.3-3: List of variables and its target in Check-list for QTO-Roughing Mill .............................. 29 Table 3.3-4: Description of Sample_1................................................................................................... 33 Table 3.3-5: Description of Sample_2................................................................................................... 34 Table 3.3-6: Severity classes for HDGL defects at SALZGITTER ...................................................... 39 Table 3.3-7: Description of the features of defect used for the correlation analysis ............................. 53 Table 3.3-8: List of features of sub-class defect of “Shell lengthen ” and “Shells” .............................. 53 Table 3.3-9: Coefficients estimate and influence degree of the parameters .......................................... 54 Table 3.3-10: Classification table for the presence/absence estimate by statistical model ................... 54 Table 3.3-11 Main process variables at HSM and their admitted range ............................................... 55 Table 3.3-12 The Eigenvectors of Prin1 for Bottom and Top defect features ...................................... 56 Table 3.3-13 Coefficients estimated and influence degree of the parameters ...................................... 57 Table 3.3-14: Check-list for the roughing mill section of representative sample of Sample_1 period 58 Table 3.3-15: Check-list for the roughing mill section of representative sample of Sample_2 period 58 Table 3.3-16: Frequency comparison of coil defect between Sample_1 and Sample_2 period ............ 60 Table 3.3-17 Hot Rolling Mill variables considered as relevant for setting up a PL cleaning model ... 60 Table 3.3-18 Skin pass variables considered as relevant for setting up a PL cleaning model ............. 61 Table 3.3-19 Pickling line variables considered as relevant for setting up a PL cleaning model ......... 61 Table 3.3-20 Training errors ................................................................................................................. 63 Table 3.3-21 Test errors ........................................................................................................................ 63 Table 3.3-22 Characteristics for a decision tree classifying defects against processing variables (True means coil with defects and False means coil below threshold defect limit. ........................................ 67 Table 3.3-23: Parameters used fort the prediction model for stickers ................................................... 68 Table 3.3-24: Results of the prediction algorithms for stickers ............................................................ 69 Table 3.3-25: Pre and Post decision subsystem used input variables at SPL ........................................ 80 Table 3.3-26: Pre and Post decision subsystem used input variables at CAPL ..................................... 81 Table 3.3-27 HSM line variables considered as relevant for real time prediction after HSM processing ............................................................................................................................................................... 94 Table 3.3-28 Pickling line variables considered as relevant for real time prediction after HSM processing .............................................................................................................................................. 94
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4
List of References 1. Blanchard, D, “Automatic surface inspection system: experience at Usinor and outlook”, REVUE DE METALLURGIE-CAHIERS D INFORMATIONS TECHNIQUES Volume: 99 Issue: 6 Pages: 551-+ Published: JUN 2002. 2. S. Moir and J. Preston, “Surface defects evolution and behaviour from cast slab to coated strip,” Journal of Materials Processing Technology, vol. 125-126, pp. 720–724, 2002. 3. H. Zheng, D. Y. Gong, G. D. Wang, X. H. Liu, and P. J. Zhang, “Software of predicting mechanical properties of strip steel by using bp networks,” Journal of Iron and Steel Research, vol. 19 (7), pp. 54–62, 2007. 4. A. Pernía-Espinoza, M. Castejón-Limas, A. González-Marcos, and V. Lobato-Rubio, “Steel annealing furnace robust neural network model,” Ironmaking and Steelmaking, vol. 32 (5), pp. 418–426, 2005. 5. L. Liu and Q. S. M. Xie, “Forecast method of steel output based on self-adaptive wavelet neural network model,” in 3rd International IEEE Conference on Intelligent Systems,, no. art. no. 4155536, 2006, pp. 834–841. 6. N. Rychtyckyj, “Intelligent systems for manufacturing at ford motor company,” IEEE Intelligent Systems, vol. 22 (1), pp. 16–19, 2007. 7. A. Loutfi and S. Coradeschi, “Odor recognition for intelligent systems,” IEEE Intelligent Systems, vol. 23 (1), pp. 41 – 48, 2008. 8. C. Longbing, V. Gorodetsky, and P. Mitkas, “Agent mining: The synergy of agents and data mining,” Intelligent Systems, IEEE, vol. 24 (3), pp. 64–72, 2009. 9. C. J. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121–167, 1998. 10. S. Urbanek, “Rserve, a fast way to provide r functionality to applications,” in Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003), 2003.
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5 5.1
Appendices Defect catalogue
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124
125
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5.2
Cost benefit calculation of developed solutions
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European Commission EUR 25070 — Improved utilisation of the results from automatic surface inspection systems (IRSIS) J. Brandenburger, M. Stolzenberg, F. Ferro, J. Diaz Alvarez, G. Pratolongo, R. Piancaldini Luxembourg: Publications Office of the European Union 2012 — 132 pp. — 21 × 29.7 cm Research Fund for Coal and Steel series ISBN 978-92-79-22218-4 doi:10.2777/21050 ISSN 1831-9424
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• Application-adapted post-processing: the data provided by the ASIS is adapted to the needs of the specific usage application. • Application-adapted grading: definition of an application-dependant quality measure based on the mass data provided by the ASIS. • Information about the life cycle of defects: analysis of the data of more than one ASIS will lead to knowledge being gained about defect evolution and development of through-process defect mappings. • Usage of ASIS results for the control of downstream processes: within this project the control of pickling line speed by means of surface inspection data will be examined. • Usage of ASIS results for the optimisation of upstream processes: by means of the correlation of ASIS results with process knowledge, defect causes and/or defect models will be created and used to optimise the upstream parameter ranges. • Usage of ASIS results for improved coil allocation: the use of developed defect mappings will enable coil allocation from an early stage.
KI-NA-25070-EN-C
With respect to customers’ ever-increasing surface quality requirements, automatic surface inspection systems (ASIS) have become more and more popular within the European flat steel sector over the last few years. As ASIS tuning formerly had the main focus, nowadays the in-depth utilisation of the results comes more to the fore, but there is no generalised approach on how to handle the data and improve their utilisation. Exactly this field is addressed by this project. The following data usage tasks are further investigated.
