Advanced width and camber control (Awicco)
Interested in European research? RTD info is our quarterly magazine keeping you in touch with main developments (results, programmes, events, etc.). It is available in English, French and German. A free sample copy or free subscription can be obtained from: Directorate-General for Research and Innovation Information and Communication Unit European Commission 1049 Bruxelles/Brussel BELGIQUE/BELGIË Fax +32 229-58220 E-mail:
[email protected] Internet: http://ec.europa.eu/research/rtdinfo.html
EUROPEAN COMMISSION Directorate-General for Research and Innovation Research Fund for Coal and Steel Unit Contact: RFCS publications Address: European Commission, CDMA 0/178, 1049 Bruxelles/Brussel, BELGIQUE/BELGIË Fax +32 229-65987; e-mail:
[email protected]
European Commission
Research Fund for Coal and Steel Advanced width and camber control (Awicco) Roger Lathe VDEh-Betriebsforschungsinstitut Sohnstraße 65, 40237 Düsseldorf, GERMANY
Adrián Espina Viella ARCELOR ESPAÑA — ArcelorMittal España S.A. Residencia de La Granda s/n, 33418 Gozón, Asturias, SPAIN
Jan Levén Metallurgical Research Institute AB Aronstorpsvägen 1, SE-974 32 Luleå, SWEDEN
Francisco Ortega Universidad de Oviedo Independencia 13, 33004 Oviedo, SPAIN
Juha Jokisaari Rautaruukki Oyj Suolakivenkatu 1, FI-00811 Helsinki, FINLAND
Wolfgang Seyruck, Gerald Hein voestalpine Stahl GmbH Voest-Alpine-Straße 3, 4030 Linz, AUSTRIA
Contract No RFSR-CT-2005-00020 1 July 2005 to 30 June 2009
Final report
Directorate-General for Research and innovation
2011
EUR 25042 EN
LEGAL NOTICE Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of the following information. .
Europe Direct is a service to help you find answers to your questions about the European Union Freephone number (*):
00 800 6 7 8 9 10 11 (*) Certain mobile telephone operators do not allow access to 00 800 numbers or these calls may be billed.
A great deal of additional information on the European Union is available on the Internet. It can be accessed through the Europa server (http://europa.eu). Cataloguing data can be found at the end of this publication. Luxembourg: Publications Office of the European Union, 2011 ISBN 978-92-79-22149-1 doi:10.2777/97730 ISSN 1831-9424 © European Union, 2011 Reproduction is authorised provided the source is acknowledged. Printed in Luxembourg Printed on white chlorine-free paper
Table of content 1 2
Final Summary ................................................................................................................... 5 Scientific and technical description of results .................................................................. 15 2.1 Comparison of initially planned activities and work accomplished. ................................ 15 2.2 Description of activities and discussion ........................................................................... 17 2.2.1 Installation of measuring devices and data acquisition .................................................... 17 2.2.1.1 Test of a mobile edge profile measuring devices.................................................... 17 2.2.1.2 Camber measuring system ...................................................................................... 18 2.2.1.3 Width and center-line strip gage. ............................................................................ 22 2.2.1.4 Integration of the new measurement data in existing data acquisition systems ...... 24 2.2.1.5 Definition of a common data base. ......................................................................... 25 2.2.1.6 Data acquisition of process data. ............................................................................ 26 2.2.1.7 Determination of rules for the validity of measuring data ...................................... 28 2.2.1.8 Collection of data in a joint data base ..................................................................... 30 2.2.2 Investigation of a new type of side guiding system to avoid camber formation. ............. 31 2.2.2.1 FEM Calculations. .................................................................................................. 32 2.2.2.2 Construction and installation of the guiding system ............................................... 38 2.2.2.3 Pilot tests and roll gap control development. .......................................................... 39 2.2.3 Development of a hybrid analytical / data based camber model ...................................... 47 2.2.3.1 Determination of parameters influencing cambering in the rougher ...................... 48 2.2.3.2 Development of the data based model for camber development prediction ........... 64 2.2.3.3 Adaptation and verification of the models using industrial data. ........................... 67 2.2.3.4 Control systems development ................................................................................. 69 2.2.3.5 Design of the controller structures .......................................................................... 73 2.2.3.6 Development of a Decision Support System for improved setup ........................... 73 2.2.3.7 Offline tests and tuning ........................................................................................... 75 2.2.3.8 Setup the necessary hardware for the controls ........................................................ 75 2.2.3.9 Programming the visualization and real time version of the control algorithms .... 75 2.2.3.10 Installation and integration in the existing control systems .................................... 76 2.2.3.11 Online test and fine tuning ...................................................................................... 78 2.2.4 Development of a hybrid analytical / data based width model ......................................... 79 2.2.4.1 Determination and quantification of the influence parameter on spread. ............... 80 2.2.4.2 Improvement of the setup model for width at RM and FM .................................... 86 2.2.4.3 Analysing the effect of strip tension and changes in the thickness profile on the spread. ..................................................................................................................... 87 2.2.4.4 Development of the Analytical Model.................................................................... 90 2.2.4.5 FEM modelling of finishing mill ............................................................................ 92 2.2.4.6 Adjusting the calculation parameters of the analytical model ................................ 97 2.2.4.7 Test of the rollgap-spread model with plant data.................................................... 98 2.2.4.8 Development of the Database model .................................................................... 100 2.2.4.9 Control system development. ............................................................................... 105 2.2.4.10 Online Model development .................................................................................. 108 2.3 Conclusions .................................................................................................................... 111 2.4 Exploitation and impact of the research results. ............................................................. 111 3 List of Figures and Tables. ............................................................................................. 113
3
4 5 5.1 5.2 5.3
List of References ........................................................................................................... 116 Appendices ..................................................................................................................... 117 Appendix 1: Additional Figures and Tables ................................................................... 117 Appendix 2: Data mining techniques. ............................................................................ 123 Appendix 3: Decision trees............................................................................................. 126
4
1
Final Summary
Objectives of the project The main objectives of the research project are summarized as follows: Installation of measuring devices at industrial facilities for measurement of bar camber and bar edge shape at the roughing mill and measurement of width between several stands at the finishing mill. Creating a common verified and valid database of all available and relevant process and strip data. Determination and quantification of the relevant parameters on spread and camber generation by doing data analysis, industrial and laboratory tests and FEM-simulations. Industrial tests for adaptation of the FEM simulation parameters to the real industrial process. Development and investigation of a new laboratory side guiding system, by means of the development of strip camber and avoidance of a wedge profile should be achieved simultaneously. Development of control system for a laboratory mill to reduce camber and thickness wedge development using the new gained side guiding measurement values. Development, adaptation and verification of a hybrid (analytical / data based) width model and a data based camber model. Development, adaptation, integration at industrial mill and verification of a modern comprehensive shape control system with two controller components for width and camber. Installation of measuring devices and data acquisition. Installation of measuring devices. To determine the influence of bar edge shape on spread it was foreseen to install a mobile measurement device at the roughing mill of VAS in a early stage of the project. Due to large vibrations, water and steam it was not possible to get the measurement in operation. Two innovative and robust camber measuring systems were installed in the roughing mill providing very accurate camber values that were essential to develop the rest of the project Characteristics • Non-contact camber measuring system based in computer vision. • Acquisition system in real time. • Possibility of working under high temperatures. • Visualization of strip shape, camber and historic values. • Data storage for future uses. • Ethernet communication with process computer. • Robustness and low maintenance. Principle of work 1. The PC receives a speed signal in order to acquire a picture each 600 mm 2. The edge of each image is calculated and then, all the edges joined form the whole border of the strip 3. Once the shape of the border is known, the camber is measured. 4. The data is sent to the process computer to be storage in a data base The system provides several parameters. • Maximum camber in each direction • Position from the head to the location of the maximum camber. • Camber in tail and head • Angle of incidence of the strip • Length of the strip
5
Another measuring system was also developed and installed in the line in order to measure the width and center-line position between stands at the finishing mill. This system is also based in computer vision and using a CCD camera is able to provide real time information. Both systems were integrated in the factory measuring network sending the data trough a TCP/IP protocol to the process computer. This information was also stored in a data base for further purposes. Data Acquisition. Before further steps were possible, the creation of a data basis was required. Data of the existing and newly installed measuring systems need to be integrated into the existing data collecting systems. Following this, data from the plants are to be collected over a statistically relevant period of time and have to unit into a common data base. The data base created in this way serves for determination and quantification of influencing parameters and for the development of the models. In a first step the data base structure and the required contents were defined. Furthermore the requirements and definitions for the data acquisition were carried out. In a second step the determination of rules for the validity of measuring data was carried out. For the development of data-based models it is vitally important, that all measured data are right and representative. Data-based models react very sensitively to wrong data. Therefore, sufficient effort must be made in this context to attain a reliable model. This was done in two ways: An expert point of view, where experts met to comment about the adequacy of the values of every variable in order to detect the most interesting ranges and A data based analysis, where the application of statistics to data produced valid ranges. The result is the creation of six values for every variable, introduced in a special table of the database: Maximum. Values over this are discarded. Very high. There is a high probability (3s) that data is wrong or point is an outlier. High. Determined by the quartile of the stored cases of the variable. And similarly, Low, Very low and Minimum Following this the extraction of data was carried out from all industrial partners. Investigation of a new type of side guiding system to avoid camber formation. Target of this project stage was the test of a new side guiding system based on a BFI’s invention. The system should provide the possibility to avoid the development of strip camber and the development of a wedge thickness profile simultaneously. The test of the system was carried out at a pilot rolling mill located at Mefos. The principles function of the system based on a special behaviour of the bar during rolling a difference in thickness reduction between the two sides of the stand (e.g. caused by a non-parallel wedge shaped roll gap). The reduction difference leads to different strip length along the strip width resulting into longitudinal cambering of the bar at the exit side. Beside this it leads to rotation of the strip on the roller table both at exit AND entry from the roll gap. The rotation takes place in direction of the stand side at which the roll gap is wider open. The behaviour leads to the idea, to use side guiding rolls equipped with measurement devices to control the roll gap during rolling. During rolling the load and the position of each roll is measured. Based on the difference in load (guide rolls Operator Side minus guide rolls Drive Side) the tilting of Example: Σ FOS > Σ FDS => hOS > hDS the rolls in the horizontal stand can be Close stand side OS and/or open stand side DS up to: Σ FOS = Σ FDS controlled. The following figure
6
demonstrates the mode of operation. FEM Calculations. First calculations with a FEM model were done to investigate the behaviour of the strip when rolling is done with different types of guide systems and with different shape of the incoming strip. The behaviour of camber formation and the strip rotation could be simulated. The conclusion of the calculations with rulers and roller guides was that they prevent rotation and side movement of the slab but does not prevent camber of the slab if the roll gap is tilted incorrectly. Furthermore the contact force between guide rolls and slab during rolling with tilted mill rolls were calculated. The force varies from ~1 kN on the guide roll farthest from the mill to ~2 kN on the roll closest to the mill. Construction and Installation of the Guiding system and Pilot Tests A roller guide system was constructed and installed at the MEFOS pilot rolling mill. A test campaign was carried out, with which the system function could be tested and verified. The test results did confirm the special behaviour of the entry rotation of the slab in case of rolling with a non-parallel roll gap and without side guiding. Furthermore it could be demonstrated that this rolling case leads to a sideway movement of the strip during rolling. Test series were carried out with aluminium samples and (hot rolled) stainless steel samples to analyse the load on the guide rolls and to develop and optimize a roll gap Rolling without side guiding Rolling with side guiding control algorithm. and roll gap control and roll gap control The tests results with side guiding roll together with the developed roll gap control did demonstrate clearly that such a system is able to avoid longitudinal camber or hook formation. The two photos exemplify the outgoing strip during rolling without side guiding and with side guiding and used control. Furthermore it could be demonstrated that the system is able to control the roll gap tilting immediately after start rolling which means a clear advantage. Based on the experiences obtained during the tests the following recommendations’ can be given: During the first tests it was realised that the friction in slide bearings of the roll holder leads to a force hysteresis between moving the rolls forward and backward. This hysteresis produces an inaccuracy of the measurements which limits the performance of the control as well as the control results. For practice it is recommended to use linear bearings (with ball bearings) instead of slide bearings or to locate the force measurement device directly behind the guide rolls. Furthermore, one disadvantage during the test procedure was the fact that the guide rolls must be separated before rolling start, so that the strip could be moved to the roll gap by the roller table. Thus the guide rolls didn’t had contact to the strip at start rolling which leads to an undefined status at this point and therefore to some delay, until the control could be switched on. Especially in practice use at least one pair of guide rolls should be driven. Development of a hybrid analytical / data based camber model Target for this model is to predict the camber to be obtained at the transfer bar in both, value and sign, based on experience gained from analysis of large set of data from precedent occasions and current data related to: Characteristics of the current slab. Mill set up at each pass. Measurements done by the camber gages at each pass, etc. Estimates supplied for this model will be used to set up the automatic camber control system, hence that the estimate should supply the amount of camber and, the most important, its sign.
7
Determination of the relevant parameters on camber generation. This task required a big effort since there is a lot of inconveniences working with imbalanced data (small defects data set among of lots of data coming from right outputs of the processes), but finally some relevant parameters were found and it was possible to develop a model for camber. First effort was dedicated to the selection of techniques for the detection of camber. As a conclusion of this test, in the final modelling of the camber detection, SVM (Support Vector Machines) and MARS (Multivariate Adaptive Regression Splines) neurally optimised will be used and ROC (Receiver Operating Characteristics) curves will be used for evaluation. (A description of the used data mining techniques is given in Annex 2) UNIOVI and ArcelorMittal performed different and parallel analyses in order to determine which the most important parameters on transfer bar camber are. Before developing the new camber measuring device the analysis was done on historical information of some 6000 bars from years 2004-2005 stored in the process database. The greatest correlations founded are: - Influence of roll thermal crown - Influence of furnaces - Mill production in tons per hour - bar temperature profile - slab width After installing the camber measuring device, analyses based in new real data were possible. For this work data from 25,000 strips were collected and only those strip with 5 passes were finally used. In order to study the effects causing camber, strips were classified in four groups: ‐ A: Convex camber, open to operator side. It is positive valued. ‐ S: S-shape camber. It has positive and negative values. ‐ U: Concave camber, open to driving side. It is negative valued. ‐ I: Correct strip It was observed that there were much more strips with A-shape than U-shaped. Also, the absolute values of camber are usually higher in the case of strips with A-shape. It is also important the influence of number of passes. A study of the data sets shows that strips programmed to more than 5 passes present less problems of camber (85% correct). Development of the data based camber model Once that the most important parameters in camber generation were known, the target was to predict the camber obtained at the transfer bar in both, value and sign, based on experience gained from analysis of large set of data from precedent occasions and current data related to: ‐ Temperatures in the different parts of the furnace, at RM entry and at VSB. ‐ Time in every part of the furnace, travelling to RM and rolling. ‐ Material properties, including 31 chemical components. ‐ Dimensional characteristics of input slab and output strip. After a complete pre-processing including data filling, outliers filtering, shape characterization, linear correlations, clustering and boosting, data was reduced to 13500 strips, as any strip affected by manual actions of the operators during rolling were eliminated. 175 variables from furnace to Rougher were selected for evaluating. With this data and using different data mining techniques (see Appendix 2) the final model was capable to classify correctly the most significant 1100 strips with only a 7,6% of false positives. After the development of a model for the camber prediction at RM exit, the next objective was the creation of a pass-by-pass model for camber in the RM. An updated dataset of around 24000 strips was used for this purpose. Firstly a camber evolution study of each pass was done based again on the ASUI classification developed in previous periods (I: no camber; A: concave camber; U: convex camber; S: S-shaped camber). It can be noticed that, after the first pass, there is hardly a sign of camber; being all mainly
8
type “I” strips. As the pass number increases, more camber problems appear, mainly type “A”. It’s important to notice how the Pass 5 present the most important concentration of S-shaped cambers, but that defect is partially corrected up to pass 7. There is a massive presence of “A” cases, growing with the number of passes, so the asymmetry is evident. The influence of feedback variables (camber and force in previous passes) keeps growing as the number of passes is higher. As a conclusion, it can be said that global model developed for the whole of passes is better since the effect of camber depends more of the global rolling pattern than of the local pass conditions. Development and integration at industrial mill of a modern comprehensive shape control. Target for this task was the development of and hybrid model for the automatic and in bar correction of the transfer bar camber and width, which will be done by tilting the stand by means of sending a corrective position signal to the position regulators of the hydraulic cylinders. The following figure gives a graphical representation of this idea. The first thing to calculate is how much profile Set up modification of the bar is necessary to correct the camber, without exceeding the maximum value of wedge admissible for the finishing mill. Once that value is determined it is necessary to estimate the tilting value to be introduced in the hydraulic cylinders in order to produce the desired change in the bar profile Determination of the tilting correction will be based on the gagemeter equation but to do not depend exclusively on the statistical model for camber estimate a multivariable model of the roughing mill was also created. With this information the model was able to predict the required roll forces to control the roughing mill in order to avoid undesired camber defects. This model was implemented in the graphic programming language LabVIEW. A PC was installed at the roughing mill for field data capturing and to run the camber correction software developed. The Pc is equipped with a PCI data acquisition board to acquire signals in real time from the roughing mill basic automation like rolling forces, the positions of both sides of rolling stand and rolling speed. The analogical signals received signals can be used in addition to other information of the present bar provided by the process computer by means of a TCP/IP interface. Software developed with Labview synchronizes the different signals, processes the data and sends the control signal to the Roughing Mill rolling control. The system also provides an application for the data visualization. Once the control was validated, it was progressively incorporated to the existing control system using a corrector factor (from 0 to 1) to reduce the possibility of undesired effects. This model has led to a significant reduction of the mill stoppages due to a better control of camber and width. The number of unscheduled mill stoppages has been reduced to one-fourth of its previous value which means an important increase in mill productivity. Development of a hybrid analytical / data based width model The target of this project stage was the avoidance of width deviations after the finishing mill. Beside this the further enhancement of an existing setup model for width at Rougher and Finishing train could be achieved based on new investigations. The main idea was to use the data mining approach to improve the results of an analytical model. In the data mining part several methods were used like artificial neural networks, decision trees or a genetic algorithm. The main concept is shown in the following figure.
9
Calculating roll force for each section and pass (considering tension)
Dividing strip into a defined number of sections
Calculating roll gap for each section and pass (considering roll force and actuators)
Collecting available and relevant process parameters (for each section)
Calculating spread for each section and pass (considering roll gap and thickness)
Calculating spread for all sections and passes (Data based System)
Calculating correction values for each section for the last vertical pass
The intention was to use the output of the analytical model as an input of the data mining model. Here other process data can be used as further inputs. The aim was to improve the results of the physical model with the information available in the other process data. A analyse of industrial data has shown that the largest width deviations and highest number of cases with a deviation do occur within the first and the last 20% of the slab length. The results did demonstrate clearly that the width deviation relative often becomes larger towards head and tail end of the slab and that the frequency of cases with a negative width deviation is higher there. The main reasons for this are most possible the differences’ in the rolling conditions between the end and middle part of the slabs. Determination and quantification of the influence parameter on spread. Complementary analyses were carried out to study factors affecting the homogeneity of width, tension and thickness were identified as the most important variables using MARS (Multivariate Adaptive Regressions-Splines) and SOM (Self organizing maps) techniques and Decision Trees were used to generate if-type rules constituting a hybrid model. Since the effect of strip tension and changes in the thickness profile were expected as main reasons for spread deviations investigation results taken from [1] were used to analyse these effects. Furthermore these results were used to start the development of the analytical Model. Improvement of the setup model for width at RM and FM. A setup model for width developed in a previous project could be improved clearly. A new training considering additional variables (mainly a more complete chemical composition including S, Ti and B content) did lead to the following result compared to previous years.
This clear improvement between the previous system and Awicco model is reflected numerically when analysing the kurtosis of the width error curve: 2004 2005 2006 2007 kurtosis -0,502007938 -0,188737708 3,097274129 3,447626597 Variables considered as the most relevant by the modelling techniques were: Width vernier used for setup Exit width calculated by the mathematical model Edge modification in the roughing mill Pass edger forces
10
Pass roll forces Chemical composition (Ceq, Si, S, Ti and B) Slab mean width Slab temperature at the exit of the furnace With current results, HSM technicians have decided to reduce the over-width in another millimetre in such a way that the reduction of width offset will be 2 mm from the beginning of the project. Considering 4Mton of coils per year and an average width of 1,100mm, direct (material) and indirect (processing, cutting, etc.) savings are very significant. In order to be ahead of future problems due to changes in product families or aging of installation, a new automated training tool was created. This approach permits adjusting the model without requirements of data mining experts and with less human effort. Development of the analytical model. In a first stage a simple model was used for the analytical model. To consider the different influence factors the Pawelski's spread equation [4] was enlarged with functions which describe the following values: Tension in conjunction with the slab thickness; measured bending force; backup roll crown calculated from the CVC Position and the measured roll force. The parameters were fitted to the best result using the Newton optimisation algorithm. The achieved accuracy of the spread calculation was low with a coefficient of determination of approximately 0.53. Then the model was used to calculate the width deviation of head and tail end of the slab. But here only a coefficient of determination of 0.01 could be achieved. In order to consider the influencing factors in a more physical based way in a second stage a model for the calculation of the roll gap contour was integrated into the development of the analytical spread model. This model calculates the deflection of a rolling mill taking into account effects like roll bending and roll shape(s) as well the compression of the roll surface. The rolling force was calculated by using Sim’s roll force model. The roll gap model was used to make first calculations of rolling tests with different tensions. It was possible to calculate which effect the strip thickness must have on the local rolling pressure distribution to obtain the spread variations and profile changes determined in the rolling tests. Furthermore the results of the calculations demonstrate that it is possible to achieve good correspondence between the calculated and measured spread values. A key prerequisite for the reliability of the calculation is to know the degree to which shifting of local rolling pressures is possible. To match the necessary calculation parameters to the rolling conditions at the plant FEM calculations were carried out. The FEM calculations did provide the spread distribution and the rolling pressure distribution over the width for the first four passes of a typical pass schedule of the finishing train. Based on these results an adjusting of the calculation parameters of the analytical model were carried out. In the next stage the analytical model was tested with plant data. To check out the model it was used to calculate the spread as well the spread deviation for the finishing train. The calculated spread was then compared with the measured spread. Even that all important parameters like roll force, strip tension, CVC position and the roll bending were considered the achieved accuracy was not good. The coefficients of determination for the spread deviation were in all cases below 0.01 which means that the calculated width deviation is almost independent form the measured width deviation. It was not possible to determine the reason(s) for the inaccuracy calculations even that several tests and modifications were done. In spite of everything the results were integrated into the database for the subsequent use for the database model. The intention was to find some more information’s regarding the possible reasons of the calculation errors. Development of the Database For the development of the Database model the following three steps were necessary: • data pre-processing • selection of the relevant variables • generation of a data mining model As the target variable for the data mining model the deviation of the strip width was selected. Here two different values were calculated:
11
• the deviation of the width of the head of the strip against the middle part, • the deviation of the width of the tail of the strip against the middle part. The selection of the relevant variables was carried out with different methods. It could be shown that there is not a very strong relation between the input and the target variables. The values of the correlation coefficient were not very high and the results of the different methods are not equal. This leads to the assumption that the relevant information’s are not present in the available data. By means of a Genetic Algorithm another approach was used to find relevant input variables. Here the fitness of the result was relative good. Based on this result several models were generated. The first attempt was a regression model for the prediction of the width deviation of the strip after the finishing mill. Some exemplarily results of the regression model are shown in the following figure.
It is obviously that the result of the validation was not sufficient. With a linear correlation coefficient of 0.56 there is an insufficient relation between the measured values and the model output. The width deviation couldn’t be estimated in a necessary manner. In a second approach a classification model was developed. Here the aim was to estimate, if the strip will be wider or smaller than the mean value of the strip. The exact value of the deviation was not estimated, only to which class it belongs to. The result of this calculation looked very promising since ~80% of the cases could be right classified. Control system development. Since no clear influence factors of the finishing train were found the only possible way to apply a control system could be to control the vertical roll gap during the last pass at the rougher. Thus for the following approach all variables were removed, that are coming from the finishing line and from the last pass of the rougher. Due to the fact only a prediction - ‘strip width will be smaller or wider at the head end’ - can be generated the following work was carried out for the this target. Even that this target is not very satisfyingly compared to the initial intended target the result can be useful to avoid negative width deviation. Especially a negative width deviation can result in a large amount of scrap. Since for the trimming process a minimum trimming width is required, a negative width deviation below a certain limit does cause the cropping of the undersized part of the head or tail end of the slab. With the same procedure as before a classification model could be generated where ~80% of the cases could be right classified. For the online Model Dynamic Link Libraries (DLL’s) were developed which provide access to the generated model. This procedure does provide on the one hand the possibility to check the functionality previously (based on MS-EXCEL) and on the other hand it is easy to skip the model to the plant systems if the tests are successful. The software system was used to simulate the application at the plant. New process data was stored into an Excel sheet and the system did calculate the classification. The data from ~11500 coils were preprocessed and the results compared with the measured width deviation. The application of the model to these data does not lead to the expected results. The classification rate decreases down to
