| Title: | Modeling of tensile test results for low alloy steels by linear regression and genetic programming taking into account the non-metallic inclusions |
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| Authors: | ID Kovačič, Miha (Author)
ID Župerl, Uroš (Author) |
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| Files: |  metals-12-01343-v4.pdf (3,72 MB)
MD5: C3147D0C9E3C072679F7E803D002486A
 https://www.mdpi.com/2075-4701/12/8/1343
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | Štore Steel Ltd. is one of the biggest flat spring steel producers in Europe. The main
motive for this study was to study the influences of non-metallic inclusions on mechanical properties
obtained by tensile testing. From January 2016 to December 2021, all available tensile strength data
(472 cases–472 test pieces) of 17 low alloy steel grades, which were ordered and used by the final
user in rolled condition, were gathered. Based on the geometry of rolled bars, selected chemical
composition, and average size of worst fields non-metallic inclusions (sulfur, silicate, aluminium
and globular oxides), determined based on ASTM E45, several models for tensile strength, yield
strength, percentage elongation, and percentage reduction area were obtained using linear regression
and genetic programming. Based on modeling results in the period from January 2022 to April 2022,
five successively cast batches of 30MnVS6 were produced with a statistically significant reduction
of content of silicon (t-test, p < 0.05). The content of silicate type of inclusions, yield, and tensile
strength also changed statistically significantly (t-test, p < 0.05). The average yield and tensile strength
increased from 458.5 MPa to 525.4 MPa and from 672.7 MPa to 754.0 MPa, respectively. It is necessary
to emphasize that there were no statistically significant changes in other monitored parameters. |
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| Keywords: | mechanical properties, tensile test, tensile strength, yield strength, percentage elongation, percentage reduction area, low alloy steel, modeling, linear regression, genetic programming, industrial study, steel making, optimization |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 30.06.2022 |
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| Article acceptance date: | 09.08.2022 |
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| Publication date: | 12.08.2022 |
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| Publisher: | MDPI AG |
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| Year of publishing: | 2022 |
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| Number of pages: | Str. 1-17 |
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| Numbering: | Vol. 12, iss. 8 (1343) |
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| PID: | 20.500.12556/DKUM-92237  |
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| UDC: | 669.1:004.9 |
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| ISSN on article: | 2075-4701 |
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| COBISS.SI-ID: | 118540547 |
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| DOI: | 10.3390/met12081343 |
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| Copyright: | © 2022 by the authors |
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| Publication date in DKUM: | 24.03.2025 |
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| Views: | 116 |
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| Downloads: | 11 |
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| Metadata: |    |
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| Categories: | Misc.
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