ISSN 1831-9424 (PDF) ISSN 1018-5593 (Printed)
Refinement of flat steel quality assessment by evaluation of high-resolution process and product data (EvalHD)
Research and Innovation
EUR 28147 EN
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EUROPEAN COMMISSION Directorate-General for Research and Innovation Directorate D — Key Enabling Technologies Unit D.4 — Coal and Steel E-mail:
[email protected] [email protected] Contact: RFCS Publications European Commission B-1049 Brussels
European Commission
Research Fund for Coal and Steel Refinement of flat steel quality assessment by evaluation of high-resolution process and product data (EvalHD)
Jens Brandenburger VDEh-Betriebsforschungsinstitut GmbH Sohnstr. 65, DE-40237 Düsseldorf, Germany
Christoph Schirm, Josef Melcher ThyssenKrupp Rasselstein GmbH Koblenzer Straße 141, DE-56626 Andernach, Germany
Floriano Ferro ILVA S.p.A Viale Certosa 2491, IT-20151 Milano, Italy
Valentina Colla, Andrea Ucci, Gianluca Nastasi, Silvia Cateni, Antonella Vignali Scuola Superiore di Studi Universitari e di Perfezionamento Sant'anna Piazza Martiri della Liberta 33, IT-56127 Pisa, Italy
Torbjörn Hansen, Jan Niemi Swerea Mefos AB Aronstorpsvägen 1, SE-971 25 Lulea, Sweden
Grant Agreement RFSR-CT-2012-00040 1 July 2012 to 31 December 2015
Final report Directorate-General for Research and Innovation
2016
EUR 28147
EN
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ISSN 1018-5593 doi:10.2777/753599 KI-NA-28-147-EN-C ISSN 1831-9424 doi:10.2777/420341 KI-NA-28-147-EN-N
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Table of contents 1
Final summary
2
5
Scientific and technical description of the results Objectives of the project Description of activities and discussion WP 1 Definition of the general framework WP 2 Implementation of HR data storage system WP 3 Methods for HR data application for quality supervision WP 4 Implementation of HR-quality supervision system Conclusions Dedicated HR data storage system Unified web-service architecture Component for the visualisation of the current production state Component for refinement of cause-and-effect analysis Component for refinement of decision support procedures Exploitation and impact of the research results
13 13 13 13 31 51 100 128 128 129 129 129 130 131
3
List of figures
133
4
List of tables
137
5
List of acronyms and abbreviations
139
6
List of references
141
Appendix 1
Measuring systems providing HR data
143
Appendix 2
Comparison of spatial DBMS
155
Appendix 3
EvalHD WMTS - Exemplary GetCapabilities response
157
Appendix 4
Evaluation tables for EvalHD solutions
161
2.1 2.2 2.2.1 2.2.2 2.2.3 2.2.4 2.3 2.3.1 2.3.2 2.3.3 2.3.4 2.3.5 2.4
3
4
1
Final summary
Task 1.1
Analysis of existing HR-data
In this first task Swerea MEFOS preliminary had to find an adequate industrial partner allowing the investigation of HR-data coming from the HSM process. Finally SSAB and their rolling mill facilities in Borlänge, Sweden agreed on participation. Existing measuring systems at the industrial sites were investigated and a detailed survey of their status and the provided type of HR data was done. The HR-data investigated within this project can be classified in three main categories (1Dcontinuous, 2D continuous and event-based). The amount of data generated by the available systems per year was calculated. Without the event-based measurements (ASIS, IDD, hole/edge crack detection), where the amount of data actually depends on the number of events occurred during production, the amount of data for all features used in EvalHD project summarized to 570 GB/year for all partners involved. Concluding it can be stated that the amount of raw HR data appeared to be manageable by modern IT-equipment. The HR data model chosen in task 2.1 was optimized regarding query performance, so further redundantly stored data was considered and data of multiple resolutions and multiple plants was stored in parallel. Finally the demands for a HR data server were defined. For the implementation of the project all project partners decided to implement a dedicated EvalHD-Server connected with the existing ITinfrastructure to minimize the workload for existing systems being used in the daily production. At MEFOS no direct connection with the data servers could be established and the data transfer had to be done on demand.
Task 1.2
Analysis of existing non-HR data
Besides the HR data, the available non-HR data and existing quality databases were further investigated regarding reasonable integration in the HR data model. At Rasselstein the existing material tracking solution was analysed to define demands for the HR data model. Therefore 2 throughprocess use-cases (“paw-scratch” and “edge-cut”) were determined to evaluate the performance of the EvalHD system and assess its accuracy with first available data in task 2.4. Further non-HR data had to be integrated in the system to allow reasonable filtering of the coils that should be visualized. This includes steel grades, thickness classes, product types etc.
Task 1.3
Evaluation of suitable interfaces, existing HR data storage and visualisation tools
To develop an advanced product quality supervisory system for flat steel production the research partners evaluated the software architecture and single components / technologies to adopt. As any HR data accumulating during the steel production process can be seen as spatial information on the product, it is related to the handling of geographic information. In this field as well opensource as commercial solutions for spatial data handling exist, so existing web-interfaces, data storage and visualisation tools have been evaluated and a survey was prepared to determine a suitable technology for further development. Finally a classical three-tier architecture as used for web-mapping applications has been chosen to separate presentation, application processing, and data management functionality. This architecture consists of a database management system (DBMS) implementing the HR-data model at the bottom that communicates with a Web-Map-Tile Server via parallel SQL queries. This Tile-Server again coordinates the communication, processes commands and performs calculation on the HRdata depending on the request coming from the browser application on top of this architecture. The communication between the browser application implementing the HM-interface and the TileServer follows a unified web-service definition based on a standard provided by the OpenGeospatial Consortium (OGC). This allows the usage of standard controls for HR data visualisation at client side. Due to the cooperation with SSAB, MEFOS had to adapt to the SSAB standards. This meant that it was demanded to use MATLAB for inter communication between SSAB data servers and the HR server and also for the visualization tasks.
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Task 2.1
HR-data storage concept
To determine the best suitable HR data model BFI, SSSA and MEFOS started with the evaluation of different approaches. However quite early it came out that the usage of spatial index structures as used by spatial DBMS and intended to be applied at project beginning, cannot achieve the desired performance. Therefore alternative approaches were investigated. The finally selected data model uses a concept of multi-scale grids and stores the data into stages of fixed resolution (coarse to fine). This means that for each coil and stage the number of tiles per stage is constant in cross- (CD) and machine direction (MD). Aggregation over multiple coils can be realized using one SQL-query for a complete grid making this approach outstanding fast. To furthermore include material tracking in this data model the data can be stored for each production step separately. The data is not only imported for the plant it is measured, but also virtually tracked through the product history and stored for each production step. Thus the data is available simultaneously in different plant coordinates enabling fast HR data access by means of redundant data storage. This data model was used for the databases located at ILVA, Rasselstein and MEFOS. BFI applied for a patent (EP 000002874034 A1) on this new approach to store and process industrial measurement data.
Task 2.2
Methods for HR-data synchronization and aggregation
One major problem of large quality databases containing data from multiple sources is data synchronisation. As the EvalHD data model is based on coil positions the first step to be done was to synchronize any time-based data to positions on the coil. This was no issue at ILVA and Rasselstein as existing quality databases could be used and these data sources provided already position-based data. However at MEFOS a lot of effort has been put into the synchronisation of the time-based data sampled at various locations along the HSM, where the speed not only varies between different positions but also at the same position during the processing of one single coil. Once position-based data is available it has to be normalized to be able to import it into the EvalHD data model. Thus the reachable accuracy of the EvalHD system is dependent on the coil dimensions and the resolution stage. Point-based measurements can be easily aggregated per grid-cell. For event-based data as well histogram-based as relative areas were used as suitable grid aggregation strategy. As relative areas are independent of coil-length they are suitable where single coils are considered and the coil-length varies, so the result can be affected by the coil-length effects. By means of trials the optimal grid parameters were determined leading to grid sizes which are multipliers of two for adjacent stages starting with 1x2 cells in stage 0. A maximum resolution of 256 x 512 grid cells (stage 8) was found to be reasonable.
Task 2.3
Implementation of task supporting tools
Within this task all required tools for the data acquisition campaigns have been developed. As in any case a dedicated server was installed to perform the data import a low workload on critical level 1 and level 2 systems could be expected. At ILVA, HR data are made available as soon as the galvanizing treatment on a coil is finished. SSSA developed a coil data transfer system which controls coil data transfer and uses a Web Service to load data about coils from ILVA database, to process and import them into the EvalHD database. The service has been designed to be very robust and it is able to recover from failed transfers of coils due to temporary networks problems or to timeouts. At Rasselstein the main challenge for the implementation of task supporting tools was to achieve Coil-“Real-Time” for the data import. That means the whole ETL procedures including material tracking evaluations have to be faster than the production. Using database optimization strategies finally double coil-real-time could be reached for HR data import. MEFOS implemented the required tools to import HR data from SSAB using MATLAB.
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Task 2.4
Data acquisition campaigns
Once the tools for data acquisition were finished, they were installed and tested at the industrial partners to ensure HR data correctness. At Rasselstein the first data acquisition campaigns were used to evaluate the use-cases defined in task 1.2 with the main aim to verify the results coming from the EvalHD system. In both cases the expected results could be also found in the EvalHD data, verifying the sufficient material tracking accuracy at Rasselstein and the HR data storage concept of EvalHD. The EvalHD server installed at Rasselstein finally contained 17.36 TB of data corresponding to 1 year of full production. The data import is following the production continuously in a ring-buffer setup. Same is true at ILVA where effective data acquisition started on January 1st, 2013. Also PL/CRM and HRM data has been included since 4th quarter of 2013. Thanks to daily access to data by Production, Control Quality, Process Engineering and Metallurgy departments, the database could be constantly supervised. Since the EvalHD server at MEFOS was not on-line with the process servers at SSAB the queries had to be performed on a small and static data base. The project was given a one day test which, due to production problems in the HSM, was reduced to one half day rendering process data from slightly less than 50 coils.
Task 3.1
Definition of semantic layers
Because of the huge amount of acquired measurement data this task dealt with methods for the reasonable data preparation of the raw data to extract meaningful information. It was investigated how far it is possible and useful to define some groups of semantically related parameters before further evaluation. Basically it came out that the majority of semantic layers that should be reasonable considered for the flat steel production process are linear combinations of equal type measures like the average, sum or difference. At Rasselstein a total of 51 semantic layers were defined mainly consisting of measurements taken at operator- and machine-side. There was one none linear semantic layer defined describing the normalized strip tension. At ILVA 144 single variables and 19 semantic layers of linear combined variables (sum or average) were used.
Task 3.2
Methods and interfaces for efficient HR-data access
Communication between the browser application implementing the HM-interface and the TileServer has to follow a unified web-service definition. Therefore standard web-services and open standards defined by the open geospatial consortium (OGC) were evaluated regarding their capabilities in the context of industrial data evaluation. The final version of a common web-service supports 4 different layer types and follows an interface structure that was based on the web-map-tile-service standard (WMTS). During the project the latest version of the web-service was always available for all partners on the project server to ease the common development. A specialized history layer supports the material tracking by providing full tracking information for each single coil. For the integration of further data filtering and preprocessing modules a service structure inspired by the WPS standard (Web-Processing Service) was foreseen. Furthermore a study on raw data integration was performed and it could be shown that using geospatial indexing could be used to include full resolution raw data as ultimate stage for the integration of ASIS data.
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Task 3.3
Methods for intelligent HR data filtering and pre-processing
Within this task, different methods were investigated for the intelligent filtering and pre-processing of HR data. This does not include data aggregation, but de-noising, outlier removal and plausibilisation of the HR data. Furthermore the handling of missing data was investigated as forth preprocessing step that was not foreseen in the proposal phase, but became important due to the grid-based data-model. Due to the EvalHD grid approach the imported data is already aggregated over the grid cell, so also the noise is already removed during grid import and no further de-noising step was required. To remove implausible data a plausibility range has been added to the import specifications. Thus data outside the plausibility ranges is not imported into the EvalHD grid structure. A lot of effort has been put in the development of efficient outlier elimination strategies. In general for the outlier removal two approaches can be applied: automatic or manual outlier removal. Concluding the actual choice mainly depends on the application of the HR data. Whereas it turned out that for the visualization of HR data a manual elimination is sufficient, for the cause-and-effect analysis the automatic removal seems to be more suitable. Similar to the outlier elimination also for the handling of grid cells with no measurement two different approaches can be selected: deletion and interpolation. Contrary to the outlier elimination in this case the more sophisticated approach is required for the visualization as without interpolation the visualization will result in an undesired streakiness, whereas the reliability of cause-and-effect analysis could suffer from artificial data introduction. Therefore it was decided to eliminate and not to interpolate grid cells with missing measurement in that case. For the visualisation the unwanted streakiness could be successfully removed using a newly invented multi-grid interpolation method as pre-processing step that allows the aggregation of sparse grid-data of multiple coils with acceptable overhead.
Task 3.4
Methods and interfaces for intelligent data visualisation
Aim of this task was to develop suitable visualisation strategies tailored to the steel production process and the visualisation of HR data on steel surfaces. Visualisation of 2D data such as temperature, thickness and flatness is by nature easily done for a single slab/strip. It’s obvious that for the presentation of such data for several slabs/strips the data somehow must be reduced in order to actually show anything of interest. Therefore for a given steel grade the highest temperature registered in the centre of each coil has to be found. The average temperature in the area around the peak temperature is calculated and then set as the IS-value for that particular coil, i.e. the temperature beyond all disturbing factors. Thus each value is normalized as follows: •
Pick the highest temperature registered in the center part of each coil, and calculate the mean value (𝜇) around this peak temperature
Calculate the temperature variance (𝜎) in the center part of the strip
Normalize each value 𝑣 to 𝑣̅ ∶=
𝑣− 𝜇 3𝜎
A similar approach has been adopted showing thickness and flatness data for multiple coils.
8
Task 3.5
Refinement of cause and effect analysis
In this task general methods applicable for a simple analysis of any kind of quality problem were developed that are tailored to the problem of continuous 2D correlation analysis and can be used without requiring any detailed user knowledge about the underlying data mining algorithms. The method development started with the intelligent pre-processing that has to provide grid data on a per-coil base and can be calculated per cell (2D) or per slice (1D) dependent on the problem that has to be analyzed. To allow the integration of spatial information in this approach also the adding of the relative positions of each cell (resp. slice) as additional features to the cause-andeffect analysis was foreseen in this concept, so that the used algorithm can also consider existing spatial in-coil correlations. Quite early in the project it turned out that already the visual presentation of synchronized HR data, as well at a single process step as over multiple production stages, combined with powerful filter capabilities of the input coil set, allows unprecedented insights into quality issues without the fundamental need to apply advanced data-mining algorithms. Consequently the visual analysis has been selected as one fundamental method for the continuous 2D correlation analysis. This implies as well the simultaneous visualisation of multiple variables over aggregated grid data as the dedicated analysis of correlations between variable pairs by means of diagrams showing process data ordered by the target value. In such cases where the visual analysis of HR data has its limits, further analysis by means of automatic procedures was investigated. Herein the use of multivariate correlation analysis by means of Self Organizing Maps (SOM) showed only indeterministic results. Various other state-of-the-art data-mining algorithms have been applied by the research partners, but also other black-box regression algorithms did not lead to satisfying results. The main problem was that the result is mostly difficult to interpret without detailed user-know-how. Therefore the consortium has chosen to focus on classification methods that are easier to interpret. Therefore at first the data has to be classified into a good and a bad quality class. Afterwards a classification algorithm can be applied to determine a classification model. The algorithm chosen was the decision tree as it provides fast training and detailed interpretation capabilities. Contrary to other black-box methods the result of a decision tree can be further analysed by a process expert without a deep knowledge of data-mining algorithms. This algorithm was enriched with a procedure to determine the most relevant process variables influencing the classifier decision. Using the HR data, different quality problems were investigated to proof the functionality of the developed system and its advantages compared to existing procedures for per-piece aggregation of the data. Within four industrial use-cases (Welding slag, Zebra pattern, Ripples, HSM Temperature) the feasibility of the developed methods for continuous 2D correlation could be successfully demonstrated and the results are presented in this report. A flatness analysis at SSAB could only be performed theoretically by Mefos as no suitable data from SSAB was available.
Task 3.6
Refinement of decision support procedures
By using results of the previous task and by dedicated investigation of coils coming from high quality production periods, a better understanding of the process enabled the development of more objective quality decisions at an early stage. Following the EvalHD approach, multi-scale grid information becomes available for decision support procedures and the aim of this task was to define methods how to use this new kind of information in a proper way to achieve a relevant refinement of the existing coil-based procedures. Because the results of the automatic classification trained at ILVA in the previous task were very promising it was decided to continue following this automatic procedure and integrate a ripple classification model to support coil decisions. It could be shown that a classifier trained using support vector machines (SVM) gives good prediction results not only on a restricted set of coils, but also on a very large dataset, demonstrating it can successfully be employed in production. At Rasselstein no comparable training data was available, so the focus was laid on the general integration of through-process HR data into an existing decision support system by means of manual rule definition. For the use of through-process HR data in discussions with the production stuff the analysis of “scale patterns” was selected as valuable use case that is perfectly suited for the development of more objective quality rules at an early state. Methods were developed, able to determine problematic hot strip series as well as the type of scale that might affect the coils concerned.
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Task 4.1
Implementation of HR data filtering and visualisation
The web-service defined in task 3.2 was implemented by the research partners to define the communication structure of the quality supervisory system. As explained in previous chapters existing server solutions could not provide sufficient performance, so a custom web-server providing the common EvalHD web-service based on the WMTS standard was implemented based on Microsoft WCF technology. A template for this implementation was commonly shared over a project web-site to ease the development. Common layers and conventions were defined to request data over this web-service. Generally a two-stage approach for querying data was selected. At first a coils-set that meets certain filter criteria is queried and afterwards event and/or measurement data from one or more data layers based on the selected coil-set is requested. SSSA and BFI implemented intuitive user interfaces making the HR data accessible using standard web-browser technology. BFI developed a client application based on XAML browser application (XBAP) technology as fewer problems with the communication and less overhead for data conversion could be expected. On the other hand the XBAP solution is dependent on Microsoft technology, requires the client-side installation of the .NET framework and is not cooperating with each internet browser (no problem arises using the MS Internet Explorer). The javascript technology is platform-independent and the user interface is easy to implement, but here problems with the communication protocol arose, because the web-server was implemented using .NET technology. Finally both technologies have certain assets and drawbacks. A running prototype implementation for both technologies is a strong argument for the universal validity of the developed solution. It has to be stressed that the common web-service finally implemented by SSSA and BFI and installed at Rasselstein and ILVA fulfils all deliverables foreseen for task 4.1. The implementation of the web-service at MEFOS was skipped as described in sec. Fehler! Verweisquelle konnte nicht gefunden werden.
Task 4.2
Implementation of cause and effect analysis
Within this task the methods for cause-and-effect analysis developed in WP3 were integrated in the product quality supervisory system for flat steel production. Due to the high flexibility of the developed solution as well a thin-client as a thick-client architecture could be implemented using the common EvalHD web-service defined in task 3.2. As the server-load at SSSA was less critical in this case a thin-client architecture where the server calculates the decision tree, seemed to be more favorable and was implemented. At Rasselstein it was chosen to relieve server load by implementing a thick-client calculating the decision tree at client-side. However in both cases the decision tree algorithm was implemented in browser applications as it combines fast training and superior result interpretation capabilities. The decision tree can be visualized in the browser application to enable detailed HR cause-and-effect analysis without the risk of misinterpretations. The approach to use the Geoserver as web-server failed due to performance reasons. To be able to implement at least a web-client communicating with a WMTS, MEFOS implemented a web-interface in Javascript and PHP code and the time to read large data sets was by that work substantially reduced. Because no relevant amount of data from SSAB could be received by MEFOS it was decided to skip the MEFOS part of D4.4 “Quality supervision component for local cause and effect analysis at HSM”. Instead by means of an in-depth analysis of the data at least a decision component for the HSM could be implemented.
Task 4.3
Implementation of decision support procedures
Within this task the methods developed in WP3 were used to create a rule-based solution for early coil quality decisions working on HR data. Depending on the individual application the solution developed generates a dedicated warning or visualises a check status (ok, warning, not ok) per coil and the quality operator in charge has to confirm this information. This quality supervision component was implemented at the HSM at SSAB, at the HDGL at ILVA and at tinplate production at Rasselstein.
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Task 4.4
Test of functionality and system tuning
Because offline test conditions with dedicated test data are never comparable with operational truth, the integration was followed by a comprehensive test phase to fix the problems that did not appear in offline tests and ensure long-term stability. Therefore a lot of tuning was performed to optimize the import services transferring HR raw data into multi-stage grid data and store it in the EvalHD data structure. The minimum requirement on import performance is always given by production throughput. The import jobs must be as fast as the incoming data as otherwise the import queues of coils waiting for data import will grow and frequently overflow resulting in data losses. At Rasselstein a performance evaluation over 2 months of visualisation usage showed less than 2 seconds median calculation time until the full requested result was presented to the user. At ILVA furthermore the transferability of the developed cause-and-effect component could be proven by a second case study analysing coils for the automotive market. The planned online test in the HSM at SSAB Borlänge plant was intended to be the functionality test as well. The preparations made by SSAB were not really up to what was expected by Swerea MEFOS. The reason is to be seen in the expected system cost. Task 4.5
Determination of economic benefit and transferability
The overall effect of operating the plants with the solutions developed within this project was evaluated with the estimation of the industrial benefits concerning reduction of operating costs, yield improvement and of course optimisation of final product surface quality. Therefore a common survey was developed finally assessing the industrial benefits of the EvalHD solution. Whereas at Rasselstein and Ilva an amortization period for the EvalHD system of roughly half a year was estimated, SSAB foresees huge effort to establish a modified IT-infrastructure able to provide position-based HR data leading to a 12-times higher amortization period. After assessment of the developed solution it was analysed concerning transferability to other plants by specification of the prerequisites for successful operation. To verify the transferability data was exchanged between the partners. Concluding it can be stated that the developed solution is transferable to any production process where HR product/process data are available. Decision support is reasonable where multiple and complex process steps are considered.
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2
Scientific and technical description of the results
2.1
Objectives of the project
The aim of this project is to develop an advanced product quality supervisory system for flat steel production based on high resolution (HR) measuring data. To achieve this ambitious target new methods have to be developed, employing innovative software technologies and modern computation capabilities, tailored to handle the massive amount of data accumulating during the steel production process. In detail the following objectives will be followed within this project:
Design and implementation of a dedicated HR data storage system capable of handling highperformance HR data access not achievable using standard data-warehouse technology
Development of a modular software system for product quality supervision supporting standardised network access to product data by definition of a unified web-service.
Implementation of this web-service at the industrial sites providing intuitive visualisations of the current production state and through-process synchronization of data coming from different production steps.
Stepwise integration of further modules, as permitted by this open technology, to reach the following advanced project objectives: o Gain of knowledge about origin and evolution of quality deficiencies by implementation of HR-cause-and-effect analysis of selected quality problems. o Accelerated root-cause identification of quality problems by integration of selected algorithms for cause and effect analysis in the HR-quality supervisory system o Optimising of the whole production chain towards zero-defect production by increased process understanding and quantification of conditions for high quality production
2.2
Description of activities and discussion
2.2.1
WP 1
Definition of the general framework
The first semester of the EvalHD project was embossed by information gathering on existing data archives and present technologies suitable for project implementation with the aim to define the demands for the HR data server and to determine the adequate technology for further development of an advanced product quality supervisory system for flat steel production based on HR measuring data. 2.2.1.1
Task 1.1 Analysis of existing HR data
Within the first semester Swerea MEFOS preliminary had to find an adequate industrial partner allowing the investigation of HR-data coming from the HSM process. Finally SSAB and their rolling mill facilities in Borlänge, Sweden agreed on participation, but the late final agreement led to a minor delay regarding the final determination of the technology for further development. The industrial partners prepared a detailed survey on existing measuring systems providing HR data that is included in Appendix 1 of this report. Thereby MEFOS focused on the HSM process at SSAB whereas ILVA investigated the measuring equipment at the new Hot Dip Galvanizing Line no. 4. At RASSELSTEIN the focus was laid on whole tinplate production process. The HR-data investigated within this project can be classified in three main categories as shown exemplary in Figure 1.
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Figure 1: Examples for HR-data types investigated in EvalHD Table 1 summarizes the HR-data considered in this project and the average amount of data per year at each partner that has to be regarded in the context of the EvalHD project. As per measuring system already some aggregations are applied in existing quality databases, for each coil attribute usually more than one feature is stored there. For instance time-based measurements are already converted to 1 m piece-wise aggregated data and the mean, maximum and minimum values are stored in the database giving 3 features for one measurement. At the beginning of the project the data at SSAB was not synchronized to coil positions and appeared as time-signals. Therefore the resolution of the measurement is given in the table instead of the spatial resolution depending on the line speed. Without the event-based measurements (ASIS, IDD, hole/edge crack detection) the amount of data for all features used in EvalHD project summarized to 570 GB/year for all partners involved. The event-based systems provide a varying amount of data and are transformed to store them grid-wise in the EvalHD databases. This yields to a constant and scalable amount of event-based data as further described in Task 2.1 Concluding it can be stated that the amount of raw HR data appeared to be manageable by modern IT-equipment. The HR data model chosen in task 2.1 was optimized regarding query performance, thus further data has to be considered for multiple resolutions and multiple plants that have to be stored in parallel. For the implementation of the project all project partners decided to implement a dedicated EvalHD-Server connected with the existing IT-infrastructure to minimize the workload for existing systems being used in the daily production. At MEFOS no direct connection with the data servers could be established and the data transfer had to be done on demand. In detail the work of MEFOS was concentrated to the continuous hot strip mill, HSM, and the reheating furnaces as well as the cooling line linked to the mill. Furthermore, parts of downstream strip treatment facilities such as tandem mill, continuous cold rolling line and cut-to-length lines were foreseen to be used for collection of process data.
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Figure 2: Layout of the Hot Strip Mill at SSAB in Borlänge, Sweden The HSM at SSAB is well equipped with measurement facilities of different types and throughout the years different new techniques have been tested, for example soft sensors and diode laser gas analysis. HR data are produced and can be collected/sampled from various parts of the HSM. All data sampled are time based. Parameters such as roll force, speed, looper angles and torque are recorded at each stand. The usage of water, like flow rates and water pressures for scale breakers after furnaces, in the rougher and before the finishing mill are stored. Flow rates pressure and water temperatures from each cooling position from roll cooling and intermediate cooling to the run out cooling table is stored. Temperatures, sampled by the use of scanning pyrometers are collected after the roughing stand, after the finishing line and after the run out cooling table. For 1-dimensional HR-data both the ARGUS and the IBA-analyser data acquisition systems are used. There are several such systems installed. Most of the HR-data is at the moment only stored for a limited time, 1 – 3 months depending on the type of data.Further downstream HR flatness data are produced by three Shapeline profile/flatness measurement systems. One is to be found after the cooling section in the continuous annealing line, another is situated after skin pass rolling in the continuous galvanising line and the third is positioned directly after the shears in a cut-tolength line. In addition there are four surface inspection systems available. Three of them are delivered by Parsytec and at least one of the systems is going to be modernised. The first is situated after the run out cooling table in the HSM and the second is placed in a recoiling line examining both sides of the strip and the third is to be found in a cut-to-length line. The fourth system is a Cognex SmartView system standing in a pickling line. That system is positioned between the pickling process and the coiler where both sides of the strip are inspected. From some of the scanning pyrometers only non-HR data is stored where a narrow selection in width direction is evaluated for its maximum and stored as a single pyrometer signal. Data is locally stored in data servers using software defined data formats. It is possible to access each database with logger specific software or by manual export to ASCII format. Some of the HR data is used to calculate other stored values e.g. mean values that are used for certain long term supervision of control systems. The data is stored weeks or months depending on the age of the system, where the system is mounted and how important the data is for the process and the statistics. Dedicated quality data is stored for at least five years, usually even longer. However this is mainly non HR data. Data is visualised both as graphical outputs as well as tabulated values. For dedicated quality parameters as well as parameters regarding critical process values alarm levels are set to alert the staff. For now there is no system connecting process/quality parameters from the different sections of the mill i.e. hot strip mill, pickling line, cold rolling mill, cut to length line etc.
15
Coil a ttribute
Bending Center shift Charge density Coating layer Conductivity Current density Flatness Flow Internal defect detection Oil layer Other pH-Value Pivoting Positioning roll Pressure Reduction ratio Residual current Rolling pressure Rotation Frequency Skin pass Speed Stretcher Leveler Strip tension Technology (IMPOC) Temperature Thickness Width Coating layer Flatness Temperature Thickness Hole/edge crack detection Internal defect detection SIS
T ype of me a sure me nt
P roduction line s e quippe d
1D-continuous 1D-continuous 1D-continuous 1D-continuous 1D-continuous 1D-continuous 1D-continuous 1D-continuous 1D-continuous 1D-continuous 1D-continuous 1D-continuous 1D-continuous 1D-continuous 1D-continuous 1D-continuous 1D-continuous 1D-continuous 1D-continuous 1D-continuous 1D-continuous 1D-continuous 1D-continuous 1D-continuous 1D-continuous 1D-continuous 1D-continuous 2D-continuous 2D-continuous 2D-continuous 2D-continuous event-based event-based event-based
3 8 4 5 7 5 4 4 2 10 12 2 3 4 3 3 4 5 1 3 17 3 15 1 12 14 12 5 3 1 3 4 3 6
Fe a ture s me a sure d
R a sse lste in 15 17 28 134 87 141 58 83 7 142 366 24 15 43 18 14 11 62 16 10 91 24 171 5 271 341 45 20 25 1 14 12 15 6
S umma ry
Fe a ture s use d in E va lH D 14 16 26 70 14 132 30 22 6 85 71 4 15 43 4 7 9 39 4 7 41 24 148 4 128 131 24 10 3 1 5 5 12 6
Avg. Me mory usa ge of use d Fe a ture s [MB/ a ]
S pa tia l R e solution
4.943 8.581 1.357 16.829 1.261 13.847 13.029 3.173 253 32.605 11.553 55 5.550 3.000 578 2.256 471 14.697 68 1.978 11.845 2.381 23.116 1.585 11.348 68.969 16.046 13.576 34.354 734 37.888
20m 1m 1m 1m 1m 1m 1m 1m 1m 1m 1-10m 1-100m 1-10m 1-10m 1m 1m 1m 10m 1m 1m 1m 1m 1-10m 1m 1-10m 1m 1m 5mm x 1m 10mm x 1m 5mm x 1m 5mm x 1m 1mm x 1mm 120mm x 5m 0.18-0.3mm x 0.3-0.7mm
357.923
PosCD x PosMD
fluctuating fluctuating fluctuating
2.299
1.137
14 25 118 30 10 6 5 2 1 2 22 18 4 12 6 1 276
14 25 118 30 10 6 5 2 1 2 22 18 4 12 6 1 276
7.972 1m 1.245 10m 5.876 10m 4.486 1m 498 10m 302 10m 251 10m 106 10m 506 1m 115 10m 2.888 10m 896 10m 200 10m 602 10m 298 1m-10m 95.689 0.18mm x 0.3mm 121.930 PosCD x PosMD
Me fos / S S AB 15 27 5 58 15 32 34 12 57 13 14 5 5 2 294
2 2 2 11 6 30 1 11 22 7 6 5 5 2 112
1.000 0,0167 mm 1.000 0,0167 mm 100 0,5*( 0.5 are counted as line-type coils while coils with 𝐿𝐼 ≤ 0.5 are classified as area-type coils. For each coil the HSM-series number is known, so the number of coils can be counted for both scale types individually and as soon as 10 coils of the same scale type occur in one HSM-series a warning as shown in Figure 96 is given to the quality engineers. This warning includes the number of coils so far considered for this HSM-series, the number of linescale affected coils and the number of area-scale affected coils. Therefore as soon as this warning appears the quality engineer can check if this was correct and take further actions for further coils coming from the same HSM-series.
Figure 96:Screenshot of HSM warning messages at Rasselstein 115
2.2.4.4
Task 4.4 Test of functionality and system tuning
As explained in task 2.4 (2.2.2.4) the EvalHD system has to cope with an enormous amount of data, emerging in the daily production. Therefore a lot of tuning was performed to optimize the import services transferring HR raw data into multi-stage grid data and store it in the EvalHD data structure. The minimum requirement on import performance is always given by production throughput. The import jobs must be as fast as the incoming data as otherwise the import queues of coils waiting for data import will grow and frequently overflow resulting in data losses. In the first version of the EvalHD data import, the import job started with an SQL query that checks for coils being unprocessed so far. As this query accesses the huge grid tables, this let to performance problems. To tune the system and prevent the grid table access an “ImportFlag” column was introduced to the coil table of the EvalHD database, combining bit-flags of all import jobs using a binary OR.
Import Job
HR-datatype
Bit
Value
LP (1-dimensional)
measurement
0
1
ASIS (defects)
event-based
1
2
Manual (defects)
event-based
2
4
QP (2-dimensional)
measurement
3
8
IDD (internal-defects)
event-based
4
16
SID (defects)
event-based
5
32
Table 22: List of import-jobs finally implemented at Rasselstein All import jobs finally installed at Rasselstein together with the associated bit-values are shown in Table 22. The resulting value of the import flag lies between 0 and 63 (OR-combination of 6 bits). Using this flag-value the unprocessed coils can be queried directly from the coil table without accessing the grid data tables leading to a reduced server load. As soon as data is imported the flag value is updated and thus the coils will not be considered for future import jobs.
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To check for query performance of the EvalHD visualisation an automatic logging functionality was implemented and for each query that is executed by the web-service the following attributes are logged in a separate database table: QueryID
Unique ID allowing assigning parallel executed queries to the same request
StartTime, CalcTime
Execution time information
Layer, Plant, Stage
Queried grid data
TileRow, TileCol
Queried grid position
Condition
Full SQL condition for debugging
NCoils
Number of queried coils
Table 23 shows a performance evaluation over 2 month for queries accessing data of at least 10 coils at Rasselstein. Here response time is the time it took until the first query of a request responded (e.g. stage 2 data for first visualisation), whereas finishing time is the time it took to complete the request and provide the full resolution (stage 8) result. This performance measurement over two months and 858 queries showed a median response time of 215 ms of the system for event-based defect data. The median to present the full resolution result took 818 ms. Furthermore this usage statistics shows that most frequently 1D process data was queried followed by event-based data. As for the 2D-continuous type the least amount of measurements was available this type currently only plays a minor role. Query type
Number Queries
Event-based
of
Med. response time (ms)
Med. finishing time (ms)
858
431
1488
1D-continuous
1070
215
818
2D-continuous
11
180
313
Table 23: Performance statistics of HR server over 2 months usage As the complexity of a DB-aggregation increases with the number of aggregated elements the next relevant question was how far the query response time depends on the number of queried coils. Figure 97 shows the result of this evaluation. It shows the query response (dark) and finishing times (light) dependant on the number of queried coils for ASIS grid information together with the linear approximation of these graphs over two months of tool usage. If more than one query was performed for a certain number of coils the median of all finishing times was used. On the one hand it can be seen in this graph that some outliers appear, especially for the finishing time that is the time until all requested data are completely provided. In the worst case this took almost one minute for a query requesting results of 7561 coils. Such outliers appear due to high server load, when e.g. the import jobs are writing grid data into the EvalHD database. This can cause some late response of the database to client requests. On the other hand the linear approximation shows only a moderate linear dependency on the number of queried coils due to fast index searching at database level.
117
Figure 97: Dependency of query calculation times and the number of queried coils. Dotted lines show the linear approximation of the median calculation times.
To test the transferability of the cause-and-effect component the system previously implemented at ILVA has been applied to a new case study, to verify the generalization of the model to coils with different characteristics or other types of defects than those used for the ripple case study presented in Task 3.5 (Sec. 2.2.3.5). Furthermore, this work was useful to make additional tests of the system functionalities and tuning. The new case study is concerning with a particular category of coils, employed for exposed parts material (EPM) production. The aim for this type of product is to have total absence of defects. Coils of the considered case study have the following characteristics: Entry or exit thickness less human errors.
Given these benefits a significant impact of the developed solution can be foreseen and the amortization time as well at Rasselstein as at Ilva for the EvalHD system is foreseen to roughly half a year. 2.2.4.5.2 Transferability The developed solution was analysed concerning transferability to other plants by specification of the prerequisites for successful operation of the EvalHD system. As first and probably most important prerequisite the central availability of all relevant quality- and process data can be seen. Only when this kind of data is accessible by ETL-procedures, it can be transferred into the EvalHD data structure and used for quality supervision purposes. To perform this transformation time-based data must be convertible to position-based data and the server processing the ETL-procedures has to be able to perform these jobs in coil-realtime, so that ETL-jobs can follow the production. Furthermore for through process analysis also all relevant material tracking information has to be available to allow the tracking and parallel storage of product and quality data over multiple production stages. Last but not least well trained quality specialists are needed, able to choose the correct filter settings and interpret the results, what is sometimes too difficult for the on-site operator. To verify the transferability of the outlier detection method, described in detail in Task 3.3 (Sec.2.2.3.3) data from Rasselstein has been sent to SSSA. The algorithm, that combines several traditional methods for outlier detection through a fuzzy inference system and that has been implemented as a C++ module, has been tested by SSSA by means of the EvalHD data provided by Rasselstein. Table 29 shows the outlier detection results on a set of 216 coils, where four HR measures have been taken into account. As in SSSA’s case study presented in Task 3.5, outlier detection has been applied coil-wise and has been omitted for lower stages, those between 0 and 3, because the number of samples for each coil is too low and thus the analysis is statistically not significant.
126
Stage
Input
Not plausible
Outliers
Outliers %
0
432
-
-
-
1
864
-
-
-
2
1728
-
-
-
3
3456
-
-
-
4
6912
-
245
3.54
5
13824
-
240
1.74
6
27648
-
310
1.12
7
55296
-
367
0.66
8
110592
-
417
0.38
Table 29: Plausible, not plausible and outlier data for a set of 216 coils from Rasselstein These results are comparable to those presented in Task 3.5 (Sec. 2.2.3.5), where ILVA data were investigated proving that the algorithm can be transferred to other scenarios. Concluding it can be stated that the developed solution is transferable to any production process where HR product/process data are available. The HR data model, the web-server and browser application are fully transferable whereas the data import has to be adapted to specific users Decision support is reasonable where multiple and complex process steps are considered. At Rasselstein it is based on an existing system, but the rule-logic is fully transferable.
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2.3
Conclusions
The activities planned for this project are mainly reached and the achieved results are promising. The main objective, to develop an advanced product quality supervisory system for flat steel production based on high resolution (HR) measuring data, could be achieved. It can be stated that constructive and effective collaboration between involved research and industrial partners has emerged a HR supervisory system of remarkable performance, which is already productively in use and by now capable to give rapid answers on many kind of different quality problems. Realized as three-tier software architecture the system is web based and can be accessed with common web browsers. Data can be presented in graphs and 2D maps as well and further components for cause-and-effect analysis and decision support were integrated. Given these benefits a significant impact of the developed solution can be foreseen and the amortization time as well at Rasselstein as at Ilva for the EvalHD system is foreseen to be roughly half a year. The EvalHD supervisory system consists of the following main components
Dedicated HR data storage system
Unified web-service architecture
Component for the visualisation of the current production state
Component for refinement of cause-and-effect analysis
Component for refinement of decision support procedures
Depending on the particular component the following conclusions can be given: 2.3.1
Dedicated HR data storage system
A new high-performance HR data model based on a multi-scale data representation over multiple production stages was developed
It provides simple and fast access to HR data enabling the in-coil aggregation of millions of quality and process measures within seconds.
It could be shown that this new concept performs far better than state-of-the-art data models in terms of query response time. Concluding it can be stated that to utilise the full potential of modern measuring equipment the basic technological concept has to be changed as an efficient statistical evaluation of multi-coil HR data is not possible using existing database infrastructures.
The query performance of the selected HR data model is only moderate linear dependent on the number of queried coils.
The minimum requirement on HR data import performance is always given by production throughput. The import jobs transferring HR data into the HR storage system must be as fast as the incoming data.
Parallel data import and querying may lead to server locks and calculation peaks when the IO-performance of the server is not sufficient
The use of plausibility thresholds already on data import is reasonable to prevent import of useless data
20 TB storage space seems to be sufficient to cover 1 year of full production data of a tinplate production site (24 plants, 1.137 HR-measurements)
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2.3.2
Unified web-service architecture
The web-map-tile-service (WMTS) standard of the open geospatial consortium (OGC) can act as basis the definition of a HR web-service
SSSA and BFI realized a WMTS compliant web service and web interface to access HR data in an efficient way
Different applications of HR-data can be supplied by the same web-service o
Summary statistics
o
Visualization
o
Root-cause-analysis
o
Decision support
The separation of the web-service architecture from client application allows the development of user-specific applications based on the same web-service
The use of quantile-based semi-automatic outlier removal is well-suited for interactive visual data investigations. For the cause-and-effect analysis the automatic removal seems to be more suitable.
The automatic outlier elimination developed by SSSA and required for black-box rootcause-analysis (RCA) is well transferable to other applications
2.3.3
Component for the visualisation of the current production state
The through-process visualization of synchronized HR data over multiple production stages could reach a significant benefit for quality engineers already from the very beginning
Whereas the in-depth analysis of HR-data previously took weeks to generate rough statements on only a few coils, the new concept is capable of providing precise results about thousands of coils within seconds.
This data refinement enables a new dimension of quality data assessment as it supports instant interactive analysis of HR data as soon as a quality problem occurs.
The developed component enables faster and more efficient investigation of quality problems and reduces the risk of misinterpretations
2.3.4
Component for refinement of cause-and-effect analysis
Three methods for continuous 2D correlation analysis have been applied: Visual analysis, multivariate correlation analysis and classification.
The visual presentation of synchronized HR data over multiple production stages combined with powerful filter capabilities of the input coil set already allows unprecedented insights into quality issues without the fundamental need to apply advanced data-mining algorithms
Assuming detailed process know-how of the user the “real-time” data evaluation opportunities provided by the EvalHD solution enable to “see” possible causes of quality deficiencies much faster, more precise and reliable than many state-of-the-art data-mining algorithm could do.
Results of multivariate correlation analysis using self-organizing maps (SOM) are difficult to interpret without detailed user-know-how (black-box). The training of the algorithm with many grid data is slow and for sliced grid data with a few process variables indeterministic behavior of the SOM results could be seen.
In contrast classification using decision trees provides fast training and detailed interpretation capabilities. Contrary to other black-box methods the result of a decision tree can be further analyzed by a process expert without a deep knowledge of data-mining algorithms.
Six industrial use case studies have been analyzed in depth by HR cause-and-effect analysis (sec. 2.2.3.5) 129
2.3.5
•
Zebra pattern (Rasselstein)
•
Welding slag (Rasselstein)
•
Ripple defect (Ilva)
•
HSM Temperature (Mefos)
•
HSM Flatness (Mefos)
•
Material for exposed parts (Ilva) (sec. 2.2.4.4)
The developed component for cause-and-effect analysis is concise, simple, significant and shareable. On the other hand skills for data preparation are required Component for refinement of decision support procedures
Methods for early coil de-allocation and HR data integration in an existing rule-based decision system have been implemented. SSSA evaluated the usage of an automatically trained decision rule at Ilva whereas the other partners implemented manual rules.
The HR usage for decision support refinement has been implemented for three industrial use-cases :
o
Hot Strip Mill: HSM temperature warning (Mefos) (sec. 2.2.4.3.1)
o
Hot dip galvanizing: Ripple defect (Ilva) (sec. 2.2.4.3.2)
o
Tinplate production: Welding slag, HSM series (Rasselstein) (sec. 2.2.4.3.3)
Compared to existing systems the integration of through-process HR data allows the application of new types of decision support rules: o
Rules dedicated to certain coil positions
o
Rules considering the material history
These rules enable faster reaction on quality problems and application of countermeasures. Therefore they increase yield and reduce scrap.
By using a rule engine the coil decisions become more factual and comprehensible. Less human errors occur and thus the surface quality is improved.
By using the temperature maps accomplished it is possible to predict what coils that most likely will be fitted with edge waves or center-buckles/quarter buckles
The developed component for HR decision support at Ilva using automatic classification is process-based and fits well with the existing systems. On the other hand the tuning of the component is an unsolved issue.
130
2.4
Exploitation and impact of the research results
Today the production of high quality steel is supported by modern measuring systems gathering an increasing amount of high resolution (HR) quality and process data along the complete flat steel production chain. Within this project a solution was developed able to cope with this huge amount of data. It could be shown that the developed HR data storage system is capable of handling highperformance HR data access not achievable using standard data-warehouse technology. Thus it can be expected that the results of this project will have significant impact on the future handling of steel production data, especially in the context of emerging factories of the future. The developed EvalHD web-server can lead as data-backbone for integrated intelligent manufacturing systems. All the identified methodologies are quite general and applicable also to different industrial scenarios and the transferability of the general concept is high. This applies as well to the application to other steelmaking issues as shown by the exposed parts material case study already approached at Ilva, as to other steelmaking plants or even to other industry fields where HR data are significant for full product characterization. BFI applied for a patent (Patent EP 000002874034 A1 (DE 102013019284 A1)) on this multi-scale data representation over multiple production stages. To ensure that all project partners are allowed to continue using their developed solutions after project end without any additional licence cost, even if the patent should be issued, a consortium agreement was established and signed by each partner. The EvalHD data structure is now “standard” at ILVA Novi Ligure. Also, it is used as reference for new implementations within ILVA Group. The ILVA personnel is now able to deal with HR data including process, product and ASIS (HDG based) and for each new machine, HR data integration is now of primary importance. Quality, Metallurgy and Production departments now work together with such data structure and gain further knowledge about the HR data usage in the daily production. To make the system robust and durable, the production is aware about importance of this kind of data; deficiencies in the system are promptly faced and corrected. Two solutions for cause-and-effect analysis and decision support have been investigated and actually implemented at ILVA Novi Ligure. Process conditions promoting the emergence of ripple defects can now be early detected and countermeasures can be taken. At Rasselstein the EvalHD quality supervision system is fully integrated in the IT infrastructure and used in the daily production. Data of 1.137 measurements of 24 plants is imported every 30 minutes (24/7). The EvalHD web-service and the XBAP - browser application are installed and running and warning messages are automatically sent to the responsible operators as soon as HR decision rules fire. This implies as well the welding slag rule that directly leads to a cleaning of the welding machine, as the HSM series warning installed and running for 2 pickling lines (Andernach + Neuwied). Finally the root-cause-analysis is fully integrated in the installed browser application to accelerated root-cause identification of quality problems. At MEFOS a web-based user interface has been accomplished, able to visualize temperature maps on individual or several strips in one single map. Although the benefits of the developed system are acknowledged by MEFOS’ industrial partner SSAB they are worried about a potential high initial investment, as existing process data system environment at the Borlänge plant does not fit well. Thus no online application of the quality supervisory system could be realised at SSAB. The dissemination activity of the research results started June 2015 with a paper presented at the European Steel Technology and Application Days at the METEC 2015 in Düsseldorf. Furthermore this project was presented at the Workshop “Industrie 4.0 in der Stahlindustrie” of the German Steel Institute VDEh, February 2016 in Düsseldorf. Furthermore a common research paper was submitted to the IFAC MMM 2016 conference, held August 2016 in Vienna. Assuming acceptance, this paper will be published in the IFACPapersOnLine series hosted on ScienceDirect and indexed in SCOPUS. Further dissemination activity of the research results is planned, e.g. at the SIS.EUROPE 2016 Surface Inspection Summit, September 2016 in Aachen.
131
List of Publications J.Brandenburger, C.Schirm, J.Melcher: “Novel Big-Data strategies for the refinement of flat steel quality assessment”, European Steel Technology and Application Days at the METEC 2015, June 2015, Düsseldorf J.Brandenburger, C.Schirm: „Big-Data-Implementierung zur Qualitätsbewertung von Flachstahlprodukten“. VDEh-Workshop Industrie 4.0 in der Stahlindustrie, February 2016, Düsseldorf J.Brandenburger, V.Colla, G.Nastasi, F.Ferro, C.Schirm, J.Melcher: "Big Data Solution for Quality Monitoring and Improvement on Flat Steel Production" 17th IFAC Symposium on Control, Optimization and Automation in Mining, Mineral and Metal Processing 2016, August 2016, Vienna (submitted) SIS.EUROPE 2016 Surface Inspection Summit. September 2016, Aachen (planned)
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3
List of figures
Figure 1: Examples for HR-data types investigated in EvalHD .................................................. 14 Figure 2: Layout of the Hot Strip Mill at SSAB in Borlänge, Sweden .......................................... 15 Figure 3: General layout of the HR data storage and visualisation system at MEFOS ................... 17 Figure 4: Layout of the hot dip galvanizing line no. 4 (ZIN4) at ILVA Novi Ligure........................ 17 Figure 5: Considered process route at ILVA ........................................................................... 18 Figure 6: Schema of HR data management at ILVA Novi Ligure ................................................ 18 Figure 7: Overview of existing IT-infrastructure and EvalHD components at Rasselstein ............. 21 Figure 8: Main aggregates of Tinplate production at Rasselstein ............................................... 22 Figure 9: Edge cut done at Rasselstein pickling line, trackable through the complete production . 23 Figure 10: Aggregated coil map with paw-scratch defects tracked to degreasing line and mean strip tension signal .................................................................................................................... 24 Figure 11: Mipmap Image Pyramid and Side-View Diagram [1] ................................................ 26 Figure 12: Web mapping architecture tested at SSSA ............................................................. 28 Figure 13: Distribution of response time for WMS requests ...................................................... 28 Figure 14: The Time Magazine Visual Hills data visualization. ................................................... 30 Figure 15: Technology chosen for further development ........................................................... 31 Figure 16: SSSA/ILVA HR database storage concept ............................................................... 32 Figure 17: Initial DB layout for the MEFOS GIS HR server........................................................ 33 Figure 18: Example of the ST_snapToGrid function. The magnification is changed by 2 steps from one map to the next one ..................................................................................................... 34 Figure 19: Variable with different resolution in x-direction. The blue line shows the original sampled variable with a length of 512 samples. The green line shows the same variable but now with 256 samples. The length of the red line is 128 samples. ................................................................ 35 Figure 20: Multigrid visualisation of “number of surfaces defects” over 5000 coils ...................... 36 Figure 21: Common EvalHD data model (Implementation at Rasselstein) .................................. 37 Figure 22: Defect percent area............................................................................................. 38 Figure 23: Defect number and % area trends ........................................................................ 39 Figure 24: Synchronisation of thickness variation for the first and the final pass in the roughing stand ................................................................................................................................ 41 Figure 25: Synchronisation of thickness variation at the roughing stand with similar data from the last stand in the finishing mill. ............................................................................................. 41 Figure 26: Comparison of raw data(upper), the EvalHD model(middle) and the modified EvalHD model(lower). .................................................................................................................... 43 Figure 27: 1D and 2D flatness profiles of one coil as displayed by BFIDataStudio ....................... 45 Figure 28: 2D-data interpolation methods of BFIDataStudio .................................................... 45 Figure 29: Use case evaluation results for paw-scratch and edge cut example (please refer to the text for further details) ....................................................................................................... 47 Figure 30: Correlation of 1D-, 2D and event-based grid data of one coil .................................... 48 Figure 31: Temperature distribution in a strip after the finishing mill obtained with a LANDSCAN 25 Hz IR camera. .................................................................................................................... 49 Figure 32: Temperature distribution in a strip after the run out cooling table obtained with a LANDSCAN 50 Hz IR camera. ............................................................................................... 49 Figure 33: Layout of the setup for the planned data acquisition campaign ................................. 50 Figure 34: Event-based measurement of internal defects at different grades and the combination to one common semantic layer ................................................................................................ 51 Figure 35: 2D measurement of thickness at operator- and machine-side and the combination to one common semantic layer ................................................................................................ 51 Figure 36: Data grouping flowchart at ILVA, from variables to semantic layers ........................... 51 133
Figure 37: Example of architecture including WPS .................................................................. 54 Figure 38: Overview on database tables of interest................................................................. 55 Figure 39: Query Execution time for the four different classes of queries ................................... 60 Figure 40: Example of implausible measurement and its effect on the visualization .................... 61 Figure 41: Top: Visualisation of paw-scratches for 504 coils with one outlier coil Bottom: Same result with manually removed outlier coil...................................................................... 62 Figure 42: Fuzzy aggregation of outlier detection algorithms ................................................... 64 Figure 43: Streakiness caused by simple aggregation of flatness data of two coils ...................... 65 Figure 44: Flatness data of Figure 43 pre-processed with multi-grid interpolation ....................... 66 Figure 45: Temperature distribution for a stainless steel hot strip prior to the run-out cooling table ........................................................................................................................................ 66 Figure 46: Temperature distribution for 100 coils at stage 3 .................................................... 67 Figure 47: 3-D super positioning of flatness, thickness and temperature for 50 coils with stage 4 resolution. ......................................................................................................................... 67 Figure 48: 2-D super positioning of flatness, thickness and temperature for 320 coils with stage 6 resolution (data of Rasselstein). ........................................................................................... 68 Figure 49: Example of aggregated tiles for stage 2 ................................................................. 69 Figure 50: Average ordered defects for a set of coils using air blowing ...................................... 70 Figure 51: Average ordered defects for a set of coils using nitrogen blowing .............................. 71 Figure 52: Threshold and empirical cumulative distribution function .......................................... 73 Figure 53: Classification algorithm ........................................................................................ 74 Figure 54: Classification algorithm enriched with mean importance computing ........................... 75 Figure 55: Visual appearance of the “Zebra pattern” defect at temper mill ................................. 76 Figure 56: Synchronized zebra pattern notifications (bottom) and normalized process variables at the temper mill of 82 coils using standard work rolls at the first stand (top: line speed (blue), rolling force first stand (black), rolling force second stand (green))........................................... 76 Figure 57: Synchronized zebra pattern notifications (bottom) and normalized process variables at the temper mill of 28 coils using higher roughness work rolls at the first stand (top: line speed (blue), rolling force first stand (black), rolling force second stand (green)) ................................ 77 Figure 58: ASIS detection of an unknown defect type at the pickling line of Rasselstein .............. 77 Figure 59: Distribution of unknown defect type over more than 100 coils: 90% of the defects were located at coil start ............................................................................................................. 77 Figure 60: Effect of using nitrogen on the occurrence of ripple defects ...................................... 79 Figure 61: Absence of defects without using any nitrogen ....................................................... 80 Figure 62: Presence of defects while using nitrogen ................................................................ 80 Figure 63: Example of a decision tree built for a set of coils using air blowing ............................ 81 Figure 64: Example of a decision tree built for a set of coils using nitrogen blowing .................... 82 Figure 65: Accuracy versus threshold in air blowing case ........................................................ 82 Figure 66: Accuracy versus threshold in nitrogen blowing case ................................................. 83 Figure 67: Accuracy versus stage in air blowing case .............................................................. 84 Figure 68: Accuracy versus stage in nitrogen blowing case ...................................................... 84 Figure 69: 2D Landscan temperature map of strip #353329 measured after the finishing mill showing the longitudinal stripes in the strip. .......................................................................... 87 Figure 70: 3D Landscan map of strip #353329 measured after the finishing mill showing longitudinal temperature stripes in the strip. The temperature differences range between 5 to 10⁰C with some areas exceeding 15⁰C. ......................................................................................... 88 Figure 71: 2D Landscan temperature map of strip # 353352 showing longitudinally as well as orthogonally oriented temperature deviations ........................................................................ 89
134
Figure 72: 3D Landscan temperature map of strip # 353352 showing longitudinally as well as orthogonally oriented temperature deviations. ....................................................................... 89 Figure 73: Strip 353352, roll forces for stand 2(p2) and stand 3(p3). ....................................... 90 Figure 74: Diagram showing, in chronological order, all the process disturbances in the HSM causing flatness errors. ....................................................................................................... 92 Figure 75: Actual coil decision path at ILVA ........................................................................... 93 Figure 76: Designed coil decision path at ILVA, at HDG exit stage ............................................ 94 Figure 77: Coil classification algorithm .................................................................................. 96 Figure 78: Aggregated scale distribution of 162 coils with line-type scale affection (top: stage 8, bottom: stage 4) ................................................................................................................ 98 Figure 79:𝑋𝑖, (𝑖 = 1. . .16) and example calculation for the line index of stage 4 coil set shown in Figure 78 bottom ............................................................................................................... 99 Figure 80: Normed amount (1 = maximum) of scale defects and line index sorted by tinning line production ......................................................................................................................... 99 Figure 81: Normed amount (1 = maximum) of scale defects and line index sorted by hot strip mill production and examples of scale distributions ..................................................................... 100 Figure 82: Web-service template shared over a Redmine-Server installed at BFI ...................... 101 Figure 83: GetCapabilities response for Plant- (left) and Measure dimension (right) .................. 102 Figure 84: GetTile-request example using various filter conditions and string encoding ............. 103 Figure 85: TileMatrixSet definition for EvalHD web-Service. Stages marked with a dot are queried by the visualization client installed at Rasselstein ................................................................. 105 Figure 86: Main components of the web client developed by SSSA ......................................... 106 Figure 87: Screenshot of the query and table widget ............................................................ 107 Figure 88: Screenshot of client interface for the measurement layer definition ......................... 107 Figure 89: Screenshot of client interface with widget defect and measurement ........................ 108 Figure 90: Screenshot of the layer selection control .............................................................. 108 Figure 91: Screenshot of the coil result list .......................................................................... 109 Figure 92: Screenshot of client interface with layer result view based on XBAP ........................ 110 Figure 93: Thick-client implementation of cause-and-effect analysis at Rasselstein ................... 112 Figure 94: The new screen layout of the Web-interface ......................................................... 113 Figure 95: Stage 2 and stage 8 distribution of welding jaw defect .......................................... 115 Figure 96:Screenshot of HSM warning messages at Rasselstein ............................................. 115 Figure 97: Dependency of query calculation times and the number of queried coils. Dotted lines show the linear approximation of the median calculation times. ............................................. 118 Figure 98: Example of coil selection through the web interface developed by SSSA .................. 119 Figure 99: Example of measures selection through the web interface developed by SSSA ......... 120 Figure 100: Example of defect types selection and visualization through the web interface developed by SSSA .......................................................................................................... 121 Figure 101: Decision tree results as shown by the web interface developed by SSSA ................ 122 Figure 102: Average ordered defects for EPM case study ....................................................... 124
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List of tables
Table 1: Summary of HR-data considered in EvalHD ............................................................... 16 Table 2: MapServer vs GeoServer - general features .............................................................. 27 Table 3: Grid definitions and exemplary grid cell sizes for a coil width of 1.000mm and a coil length of 10.000m ....................................................................................................................... 36 Table 4: Number of grid entries per stage ............................................................................. 40 Table 5: Estimation of possible synchronisation accuracy for Mefos model and the EvalHD model. 42 Table 6: Comparison of old and new EvalHD-Server setup at Rasselstein .................................. 48 Table 7: Summary of semantic layers defined for EvalHD ........................................................ 52 Table 8: Number of records of tables .................................................................................... 59 Table 9: Size of tables and indexes ....................................................................................... 59 Table 10: Example of plausible, not plausible and outlier data for a set of 177 coils .................... 64 Table 11: Example of dataset matrix for a set of two coils related to stage 2 ............................. 69 Table 12: Results of two SOM runs on the same dataset (2D-analysis)...................................... 72 Table 13: Confusion matrix for binary classification ................................................................ 73 Table 14: Decision tree accuracy on a set of about 180 coils .................................................... 81 Table 15: Process variable mean importance in air blowing ...................................................... 85 Table 16: Process variable mean importance in nitrogen blowing.............................................. 86 Table 17: Accuracy comparing using all or the selected subset of the process variables under consideration ..................................................................................................................... 87 Table 18: Example of dataset matrix for a set of two coils related to stage 2 ............................. 95 Table 19: Coil Classification accuracy for ripple case study ...................................................... 96 Table 20: Confusion matrix of the validation set for case A ...................................................... 96 Table 21: Confusion matrix of the validation set for case B ...................................................... 97 Table 22: List of import-jobs finally implemented at Rasselstein ............................................. 116 Table 23: Performance statistics of HR server over 2 months usage ........................................ 117 Table 24: Plausible, not plausible and outlier data for a set of 297 coils in EPM case study ........ 121 Table 25: Classification accuracy for EPM case study............................................................. 122 Table 26: Process variable mean importance in EPM case study ............................................. 123 Table 27: Subset of more important process variables in EPM case study ................................ 123 Table 28: Accuracy comparing using all or the selected subset of the process variables under consideration in EPM case study ......................................................................................... 124 Table 29: Plausible, not plausible and outlier data for a set of 216 coils from Rasselstein ......... 127
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5
List of acronyms and abbreviations
ADO
ActiveX Data Objects
AGC
Automatic Gap Control
AHSS
Advanced High Strength Steel
AJAX
Asynchronous JavaScript and XML
API
Application programming interface
ASCII
American Standard Code for Information Interchange
ASIS
Automatic Surface Inspection System
AUC
Area Under Curve
BCR
Balanced Classification Rate
BFI
Betriebsforschungsinstitut
CCA
Coil Classification Accuracy
CD
Cross Direction
CE
Cause/Effect
CORS
Cross-origin resource sharing
CPU
Central Processing Unit
CRM
Cold Rolling Mill
DB
Database
DBMS
Database Management Systems
ECDF
Empirical Cumulative Distribution Function
EPM
Exposed Parts Material
ETL
Extract, Transform and Load
EU
European Union
FC
Final Cooling
FCM
Fuzzy C-means
FN
False Negative
FP
False Positive
GB
Giga byte
GIN
Generalized Inverted index
GIS
Geographic Information Systems
GIST
Generic Index Structure
GML
Geography Markup Language
GPL
General Public License
GUI
Graphical User Interface
HDD
Hard Disk Drive
HDG
Hot Dip Galvanizing
HDGL
Hot Dip Galvanizing Line
HF
High Frequency
HM
Human-Machine
HR
High Resolution
HRM
Hot Rolling Mill
HSM
Hot Strip Mill
HTTP
Hypertext Transfer Protocol
ID
Identifier
IDD
Internal Defect Detection
IIS
Internet Information Services
IO
Input-Output
IR
Infrared
JSON
JavaScript Object Notation
KML
Keyhole Markup Language
KVP
Key Value Pair
LF
Low Frequency
LI
Line Index
LOF
Local Outlier Factor 139
LP
Length Profile
MB
Megabyte
MC
Mobile Cooler
MD
Machine Direction
MIT
Massachusetts Institute of Technology
MS
Machine Side
OGC
Open Geospatial Consortium
OLAP
On-Line Analytical Processing
OS
Operator Side
PC
Personal Computer
PHP
Hypertext Preprocessor
PL
Pickling Line
QP
Cross Profile
RAM
Random-Access Memory
RC
Rapid Cooling
RCA
Root Cause Analysis
REST
Representational State Transfer
ROT
Run Out Table
SID
Specialized surface inspection system
SIS
Surface Inspection System
SLD
Styled Layer Descriptor
SOAP
Simple Object Access protocol
SOM
Self Organizing Map
SP
Space Partitioning
SQL
Structured Query Language
SSAB
Swedish steel producer
SSSA
Scuola Superiore di Studi Universitari e di Perfezionamento Sant'anna
SVM
Support Vector Machine
TB
Terabyte
TCC
Termal Crown Compensation
TN
True Negative
TP
True Positive
URI
Uniform resource identifier
URL
Uniform resource locator
US
United States
VPN
Virtual Private Network
WCF
Windows Communication Foundation
WCS
Web Coverage Service
WFS
Web Feature Service
WMC
Web Map Contex
WMS
Web Map Service
WMTS
Web Map Tile Service
WP
Work Package
WPF
Windows Presentation Foundation
WPS
Web Processing Service
XAML
Extensible Application Markup Language
XBAP
XAML Browser Applications
XML
Extensible Markup Language
XSD
XML Schema Definition
140
6
List of references
[1]
Tanner, Migdal, Jones: „The Clipmap: A virtual Mipmap“ In Computer Graphics (Proceedings of SIGGRAPH 98), pp. 151–158.
[2]
Katibah, Stojic: “New Spatial Features in SQL Server Code-Named “Denali”, SQL Server Technical Article, July 2011
[3]
Improved monitoring and control of flat steel surface quality and production performance by utilisation of results from automatic surface inspection systems (SISCON), RFSR-CT2009-00034, 2009-2012. Vyacheslav Tuzlukov (2010), Signal Processing Noise, Electrical Engineering and Applied Signal Processing Series, CRC Press. ISBN 9781420041118
[4] [5]
S. Cateni & V. Colla & G. Nastasi. A multivariate fuzzy system applied for outliers detection. Journal of Intelligence & Fuzzy System, 24:889–903, 2013.
[6]
T.Mitchell, "Machine Learning", ISBN 0070428077, McGraw Hill, 1997
[7]
L. Rokach and O. Maimon. Data mining with decision trees: theory and applications. Pub Co Inc., ISBN 978-9812771711 (2008).
[8]
Taylor JR. An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements. University Science Books. 1999.
[9]
Vannucci, M. Colla, V. Novel classification method for sensitive problems and uneven datasets based on neural networks and fuzzy logic. Applied Soft computing Journal, 11(2), 2011, pp. 2383-2390
[10]
Sokolova, M. and Lapalme, G. 2009. A systematic analysis of performance measures for classification tasks. Inf. Process. Manage. 45, 4 (Jul. 2009), 427-437.
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142
Appendix 1 Measuring systems providing HR data
143
Rasselstein
144
145
146
147
148
Ilva
149
150
Mefos
151
152
153
154
Appendix 2 Comparison of spatial DBMS
155
156
Appendix 3
EvalHD WMTS - Exemplary GetCapabilities response
157
158
159
160
Appendix 4 Evaluation tables for EvalHD solutions
161
Rasselstein
162
Ilva
163
Mefos
164
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KI-NA-28-147-EN-N
The aim of this project was to develop an advanced product quality supervisory system for flat steel production based on high resolution (HR) measuring data. To achieve this ambitious target new methods have been developed, employing innovative software technologies and modern computation capabilities, tailored to handle the massive amount of data accumulating during the steel production process. Based on a multi-scale data representation over multiple production stages the concept finally developed provides simple and fast HR data access. Realized as a three-tier software architecture including a web-service for a standardized data access, the potential of this new concept could be remarkably proven and further modules were stepwise integrated. At first a module for efficient through process data visualisation that enables the in-coil aggregation of millions of quality and process measures within seconds could be realized to gain knowledge about origin and evolution of quality deficiencies and allow instant interactive data analysis as soon as a quality problem occurs. Furthermore a component for advanced cause-and-effect analysis based on HR data was realized to accelerate root-cause identification of quality problems and decision support rules were implemented based on HR data. It could be shown that this new concept performs far better than state-of-theart data models in terms of query response time. First experiences with this new system showed that it is able to provide precise results about thousands of coils within seconds, whereas previously the in-depth analysis of HR-data could take weeks to generate only rough statements on a few coils.
ISBN 978-92-79-62952-5 doi:10.2777/420341
