Optimization of the Continuous Casting Process of Hypoeutectoid Steel Grades Using Multiple Linear Regression and Genetic Programming—An Industrial Study

Štore Steel Ltd. is one of the major flat spring steel producers in Europe. Until 2016 the company used a three-strand continuous casting machine with 6 m radius, when it was replaced by a completely new two-strand continuous caster with 9 m radius. For the comparison of the tensile strength of 41 h...

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Main Authors: Miran Brezocnik, Uroš Župerl
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Metals
Subjects:
Online Access:https://www.mdpi.com/2075-4701/11/6/972
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author Miran Brezocnik
Uroš Župerl
author_facet Miran Brezocnik
Uroš Župerl
author_sort Miran Brezocnik
collection DOAJ
description Štore Steel Ltd. is one of the major flat spring steel producers in Europe. Until 2016 the company used a three-strand continuous casting machine with 6 m radius, when it was replaced by a completely new two-strand continuous caster with 9 m radius. For the comparison of the tensile strength of 41 hypoeutectoid steel grades, we conducted 1847 tensile strength tests during the first period of testing using the old continuous caster, and 713 tensile strength tests during the second period of testing using the new continuous caster. It was found that for 11 steel grades the tensile strength of the rolled material was statistically significantly lower (<i>t</i>-test method) in the period of using the new continuous caster, whereas all other steel grades remained the same. To improve the new continuous casting process, we decided to study the process in more detail using the Multiple Linear Regression method and the Genetic Programming approach based on 713 items of empirical data obtained on the new continuous casting machine. Based on the obtained models of the new continuous casting process, we determined the most influential parameters on the tensile strength of a product. According to the model’s analysis, the secondary cooling at the new continuous caster was improved with the installation of a self-cleaning filter in 2019. After implementing this modification, we performed an additional 794 tensile tests during the third period of testing. It was found out that, after installation of the self-cleaning filter, in 6 steel grades out of 19, the tensile strength in rolled condition improved statistically significantly, whereas all the other steel grades remained the same.
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spelling doaj.art-775cf08794a24198b1a735db3fb9287e2023-11-22T00:33:32ZengMDPI AGMetals2075-47012021-06-0111697210.3390/met11060972Optimization of the Continuous Casting Process of Hypoeutectoid Steel Grades Using Multiple Linear Regression and Genetic Programming—An Industrial StudyMiran Brezocnik0Uroš Župerl1Faculty of Mechanical Engineering, University of Maribor, 2000 Maribor, SloveniaFaculty of Mechanical Engineering, University of Maribor, 2000 Maribor, SloveniaŠtore Steel Ltd. is one of the major flat spring steel producers in Europe. Until 2016 the company used a three-strand continuous casting machine with 6 m radius, when it was replaced by a completely new two-strand continuous caster with 9 m radius. For the comparison of the tensile strength of 41 hypoeutectoid steel grades, we conducted 1847 tensile strength tests during the first period of testing using the old continuous caster, and 713 tensile strength tests during the second period of testing using the new continuous caster. It was found that for 11 steel grades the tensile strength of the rolled material was statistically significantly lower (<i>t</i>-test method) in the period of using the new continuous caster, whereas all other steel grades remained the same. To improve the new continuous casting process, we decided to study the process in more detail using the Multiple Linear Regression method and the Genetic Programming approach based on 713 items of empirical data obtained on the new continuous casting machine. Based on the obtained models of the new continuous casting process, we determined the most influential parameters on the tensile strength of a product. According to the model’s analysis, the secondary cooling at the new continuous caster was improved with the installation of a self-cleaning filter in 2019. After implementing this modification, we performed an additional 794 tensile tests during the third period of testing. It was found out that, after installation of the self-cleaning filter, in 6 steel grades out of 19, the tensile strength in rolled condition improved statistically significantly, whereas all the other steel grades remained the same.https://www.mdpi.com/2075-4701/11/6/972continuous castinghypoeutectoid steeltensile strengthmodelingoptimizationmultiple linear regression
spellingShingle Miran Brezocnik
Uroš Župerl
Optimization of the Continuous Casting Process of Hypoeutectoid Steel Grades Using Multiple Linear Regression and Genetic Programming—An Industrial Study
Metals
continuous casting
hypoeutectoid steel
tensile strength
modeling
optimization
multiple linear regression
title Optimization of the Continuous Casting Process of Hypoeutectoid Steel Grades Using Multiple Linear Regression and Genetic Programming—An Industrial Study
title_full Optimization of the Continuous Casting Process of Hypoeutectoid Steel Grades Using Multiple Linear Regression and Genetic Programming—An Industrial Study
title_fullStr Optimization of the Continuous Casting Process of Hypoeutectoid Steel Grades Using Multiple Linear Regression and Genetic Programming—An Industrial Study
title_full_unstemmed Optimization of the Continuous Casting Process of Hypoeutectoid Steel Grades Using Multiple Linear Regression and Genetic Programming—An Industrial Study
title_short Optimization of the Continuous Casting Process of Hypoeutectoid Steel Grades Using Multiple Linear Regression and Genetic Programming—An Industrial Study
title_sort optimization of the continuous casting process of hypoeutectoid steel grades using multiple linear regression and genetic programming an industrial study
topic continuous casting
hypoeutectoid steel
tensile strength
modeling
optimization
multiple linear regression
url https://www.mdpi.com/2075-4701/11/6/972
work_keys_str_mv AT miranbrezocnik optimizationofthecontinuouscastingprocessofhypoeutectoidsteelgradesusingmultiplelinearregressionandgeneticprogramminganindustrialstudy
AT uroszuperl optimizationofthecontinuouscastingprocessofhypoeutectoidsteelgradesusingmultiplelinearregressionandgeneticprogramminganindustrialstudy