Open Access

ARTICLE

Application of Artificial Neural Networks in Design of Steel Production Path

by Igor Grešovnik¹, Tadej Kodelja¹, Robert Vertnik², Bojan Sen i ˇc³, Božidar Šarler¹

1 Centre of Excellence for Biosensors, Instrumentation and Process Control, Solkan, Slovenia. igor.gresovnik@cobik.si, tadej.kodelja@cobik.si, www.cobik.si
2 University of Nova Gorica, Nova Gorica, Slovenia. bozidar.sarler@ung.si, www.ung.si
3 Štore Steel, Štore, Slovenia. robert.vertnik@store-steel.si, bojan.sencic@store-steel.si, miha.kovacic@store-steel.si, www.store-steel.si
4 Corresponding author. Institute of Metals and Technology, Ljubljana, Slovenia

Computers, Materials & Continua 2012, 30(1), 19-38. https://doi.org/10.3970/cmc.2012.030.019

Abstract

Artificial neural networks (ANNs) are employed as an alternative to physical modeling for calculation of the relations between the production path process parameters (melting of scrap steel and alloying, continuous casting, hydrogen removal, reheating, rolling, and cooling on a cooling bed) and the final product mechanical properties (elongation, tensile strength, yield stress, hardness after rolling, necking) of steel semi products. They provide a much faster technique of response evaluation complementary to physical modeling. The Štore Steel company process path for production of steel bars is used as an example for demonstrating the approach. The applied ANN is of a multilayer feedforward type with sigmoid activation function and supervised learning. The entire set of 123 process parameters has been reduced to 34 influential ones and 1879 data sets from the production line have been used for learning. The results of parametric studies performed on the ANN based model seem consistent with the expectations based on industrial experiences. However, further improvements in data acquisition and analytical procedures are envisaged in order to obtain a methodology, reliable enough for use in the everyday industrial practice. The methodology seems to be for the first time applied in the through process modeling of steel production.

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Keywords

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