Modeling and Optimization of Steel Machinability with Genetic Programming: Industrial Study
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 conti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/11/3/426 |
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| author | Miha Kovačič Shpetim Salihu Gašper Gantar Uroš Župerl |
| author_facet | Miha Kovačič Shpetim Salihu Gašper Gantar Uroš Župerl |
| author_sort | Miha Kovačič |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T05:21:16Z |
| format | Article |
| id | doaj.art-7310c540476b4453b14e39d6b89aad06 |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-09T05:21:16Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-7310c540476b4453b14e39d6b89aad062023-12-03T12:40:16ZengMDPI AGMetals2075-47012021-03-0111342610.3390/met11030426Modeling and Optimization of Steel Machinability with Genetic Programming: Industrial StudyMiha Kovačič0Shpetim Salihu1Gašper Gantar2Uroš Župerl3ŠTORE STEEL, d.o.o., Research and Development, 3220 Štore, SloveniaLaboratory for Mechatronics, Laboratory for Machining, University of Maribor, Faculty of Mechanical Engineering, 2000 Maribor, SloveniaCollege of Industrial Engineering, 3000 Celje, SloveniaLaboratory for Mechatronics, Laboratory for Machining, University of Maribor, Faculty of Mechanical Engineering, 2000 Maribor, SloveniaIn 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.https://www.mdpi.com/2075-4701/11/3/426calcium-treated steelsteelmakingsecondary metallurgycontinuous castingmachinabilitymodeling |
| spellingShingle | Miha Kovačič Shpetim Salihu Gašper Gantar Uroš Župerl Modeling and Optimization of Steel Machinability with Genetic Programming: Industrial Study Metals calcium-treated steel steelmaking secondary metallurgy continuous casting machinability modeling |
| title | Modeling and Optimization of Steel Machinability with Genetic Programming: Industrial Study |
| title_full | Modeling and Optimization of Steel Machinability with Genetic Programming: Industrial Study |
| title_fullStr | Modeling and Optimization of Steel Machinability with Genetic Programming: Industrial Study |
| title_full_unstemmed | Modeling and Optimization of Steel Machinability with Genetic Programming: Industrial Study |
| title_short | Modeling and Optimization of Steel Machinability with Genetic Programming: Industrial Study |
| title_sort | modeling and optimization of steel machinability with genetic programming industrial study |
| topic | calcium-treated steel steelmaking secondary metallurgy continuous casting machinability modeling |
| url | https://www.mdpi.com/2075-4701/11/3/426 |
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