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Article

Modeling and Optimization of Steel Machinability with Genetic Programming: Industrial Study

1
ŠTORE STEEL, d.o.o., Research and Development, 3220 Štore, Slovenia
2
Laboratory for Fluid Dynamics and Thermodynamics, University of Ljubljana, Faculty of Mechanical Engineering, 1000 Ljubljana, Slovenia
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College of Industrial Engineering, 3000 Celje, Slovenia
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Laboratory for Mechatronics, Laboratory for Machining, University of Maribor, Faculty of Mechanical Engineering, 2000 Maribor, Slovenia
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Environmental Protection College, 3320 Velenje, Slovenia
*
Author to whom correspondence should be addressed.
Academic Editor: Henrik Saxen
Metals 2021, 11(3), 426; https://doi.org/10.3390/met11030426
Received: 12 January 2021 / Revised: 26 February 2021 / Accepted: 28 February 2021 / Published: 5 March 2021
(This article belongs to the Special Issue Applied Artificial Intelligence in Steelmaking)
In this paper, machinability influences from the start to end of final product production in a steel plant were analyzed, including chemical composition, deoxidizing agents and casting parameters, which drastically influence the macrostructure and segregation (i.e., chemical nonhomogeneity) of continuously cast and subsequently rolled material. The data (seven parameters from secondary metallurgy, four parameters from the casting process and the content of ten chemical elements) from the serial production of calcium-treated steel grades (254 batches of 25 different steel grades from January 2018 to March 2020) were used for predicting machinability. Machinability was determined based on ISO 3685:1993, where the machinability of each individual batch is represented as the cutting speed and the tool is worn out within fifteen minutes. For the prediction of these cutting speeds, linear regression and genetic programming were used. Out of 25 analyzed steel grades, 20MnV6 steel grade was the most problematic and also the most often produced. Out of 57 produced batches of 20MnVS6 steel, 23 batches had nonconforming machinability. Based on the modeling results, the steelmaking process was optimized. Consequently, 40 additional batches of 20MnV6 (from March 2020 to July 2020) were subsequently produced based on an optimized steelmaking process. In all 40 cases, the required machinability was achieved without changing other properties required by the customers. View Full-Text
Keywords: calcium-treated steel; steelmaking; secondary metallurgy; continuous casting; machinability; modeling; genetic programming; linear regression; optimization; industrial study calcium-treated steel; steelmaking; secondary metallurgy; continuous casting; machinability; modeling; genetic programming; linear regression; optimization; industrial study
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MDPI and ACS Style

Kovačič, M.; Salihu, S.; Gantar, G.; Župerl, U. Modeling and Optimization of Steel Machinability with Genetic Programming: Industrial Study. Metals 2021, 11, 426. https://doi.org/10.3390/met11030426

AMA Style

Kovačič M, Salihu S, Gantar G, Župerl U. Modeling and Optimization of Steel Machinability with Genetic Programming: Industrial Study. Metals. 2021; 11(3):426. https://doi.org/10.3390/met11030426

Chicago/Turabian Style

Kovačič, Miha, Shpetim Salihu, Gašper Gantar, and Uroš Župerl. 2021. "Modeling and Optimization of Steel Machinability with Genetic Programming: Industrial Study" Metals 11, no. 3: 426. https://doi.org/10.3390/met11030426

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