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Artificial Neural Network for Steel Production Design: Process Modeling and Parameter Estimation

This presentation discusses the use of artificial neural networks (ANN) in the design of steel production processes through process modeling. The main goals are to connect process parameters with material properties, such as elongation, tensile strength, yield stress, hardness, and necking. The presentation also covers error estimation, parametric tests, and the application of ANN in the steel production chain. The conclusion highlights the successful demonstration of the methodology and identifies areas for improvement, such as data acquisition and understanding the quality of ANN.

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Artificial Neural Network for Steel Production Design: Process Modeling and Parameter Estimation

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  1. TOPMOST STEEL PRODUCTION DESIGN BY USING ARTIFICIAL NEURAL NETWORK THROUGH PROCESS MODELINGTadej KodeljaIgor Grešovnik, Robert Vertnik, Božidar ŠarlerLaboratory for Advanced Materials Systems(Centre of Excellence for Biosensors, Instrumentation and Process Control)Laboratory for Multiphase Processes(University of Nova Gorica, Institute of Metals and Iechnology)

  2. Scope of Presentation • Through process modeling • Process parameters • Use of artificial neural network – ANN • Error estimation and parametric tests • Conclusion and further work

  3. Štore Steel Process SchemeMain Goals of Through Process Modeling • PROCESS PARAMETERS • 151 • MATERIAL PROPERTIES • Elongation • Tensile strength • Yield Stress • Hardness • Necking

  4. Steel Process Route Modeling Scheme MAIN CONCEPT Combination of physical modeling and artificial intelligence modeling PROCESSES • Casting • Hydrogen Removal • Reheating • Rolling Mill • Heat Treatment

  5. Artificial Intelligence Modeling Artificial Neural Network - ANN • Mathematical model inspired by the structure of biological neural networks • ANN learns by example as do their biological counterparts • ANN are typically organized in layers (Input, Hidden, Output) • Implemented open source library (Aforge.net) • FeedforwardBackpropagation algorithm • Supervised learning (known Inputs and outputs) • Multylayer architecture

  6. Process Parameters and Properties PROCESS OUTPUT VALUES • A (%) – Elongation • Rm (N/mm2) – Tensile strength • Rp0,2 (N/mm2) – Yield Stress • HB – Hardness After Rolling • Z (%) – Necking Influential parameters have been selected based on expert knowledge of technologists in Štore Steel.

  7. ANN for Steel Production Chain • Separate training data for 2 dimensions (140 mm, 180 mm) • Parameters for training (34 input, 5 output, 1879 training sets, 94 verification sets) • 100.000 ANN training cycles • Training performed on aworkstation with 12 processor cores (Xeon 5690 3.47GHz ) • Training time ~ 20 hours • Response evaluation times in range of 1/100 s (suitable for optimization)

  8. Errors in Verification Points Relative error in verification points for elongation

  9. Parametric Studies • Steel hardness after rolling as a function of the carbon mass fraction Calculated on 2 training and 2 verification sets Calculated on 2 real sets and 18 calculated sets on the line between them

  10. Conclusions • Connect process parameters with final material properties • Methodology has been successfully demonstrated • The problems lie in details such as • Lack of sensors where data should be collected • Lack of systematic data acquisition • Lack of understanding the quality of ANN • Which process parameters influence on the predefined product properties? • Grešovnik, I.; Kodelja, T.; Vertnik, R.; Senčič, B.; Kovačič, M.; Šarler, B. Application of artificial neural network in design of steel production path. Computers, Materials & Continua, 2012, In press.

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