Modelling of the Steel High-Temperature Deformation Behaviour Using Artificial Neural Network
Hot forming is an essential part of the manufacturing of most steel products. The hot deformation behaviour is determined by temperature, strain rate, strain and chemical composition of the steel. To date, constitutive models are constructed for many steels; however, their specific chemical composit...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/12/3/447 |
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| author | Alexander Churyumov Alena Kazakova Tatiana Churyumova |
| author_facet | Alexander Churyumov Alena Kazakova Tatiana Churyumova |
| author_sort | Alexander Churyumov |
| collection | DOAJ |
| description | Hot forming is an essential part of the manufacturing of most steel products. The hot deformation behaviour is determined by temperature, strain rate, strain and chemical composition of the steel. To date, constitutive models are constructed for many steels; however, their specific chemical composition limits their application. In this paper, a novel artificial neural network (ANN) model was built to determine the steel flow stress with high accuracy in the wide range of the concentration of the elements in high-alloyed, corrosion-resistant steels. The additional compression tests for stainless Cr12Ni3Cu steel were carried out at the strain rates of 0.1–10 s<sup>−1</sup> and the temperatures of 900–1200 °C using thermomechanical simulator Gleeble 3800. The ANN-based model showed high accuracy for both training (the error was 6.6%) and approvement (11.5%) datasets. The values of the effective activation energy for experimental (410 ± 16 kJ/mol) and predicted peak stress values (380 ± 29 kJ/mol) are in good agreement. The implementation of the constructed ANN-based model showed a significant influence of the Cr12Ni3Cu chemical composition variation within the grade on the flow stress at a steady state of the hot deformation. |
| first_indexed | 2024-03-09T13:19:32Z |
| format | Article |
| id | doaj.art-81c728b630d7438880edf6a9587ccdfc |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-09T13:19:32Z |
| publishDate | 2022-03-01 |
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| record_format | Article |
| series | Metals |
| spelling | doaj.art-81c728b630d7438880edf6a9587ccdfc2023-11-30T21:31:17ZengMDPI AGMetals2075-47012022-03-0112344710.3390/met12030447Modelling of the Steel High-Temperature Deformation Behaviour Using Artificial Neural NetworkAlexander Churyumov0Alena Kazakova1Tatiana Churyumova2Department of Physical Metallurgy of Non-Ferrous Metals, National University of Science and Technology “MISiS”, Leninskiy Prospekt 4, 119049 Moscow, RussiaDepartment of Physical Metallurgy of Non-Ferrous Metals, National University of Science and Technology “MISiS”, Leninskiy Prospekt 4, 119049 Moscow, RussiaJoint-Stock Company “Advanced Research Institute of Inorganic Materials named after Academician A.A. Bochvar”, Rogova Str. 5a, 123098 Moscow, RussiaHot forming is an essential part of the manufacturing of most steel products. The hot deformation behaviour is determined by temperature, strain rate, strain and chemical composition of the steel. To date, constitutive models are constructed for many steels; however, their specific chemical composition limits their application. In this paper, a novel artificial neural network (ANN) model was built to determine the steel flow stress with high accuracy in the wide range of the concentration of the elements in high-alloyed, corrosion-resistant steels. The additional compression tests for stainless Cr12Ni3Cu steel were carried out at the strain rates of 0.1–10 s<sup>−1</sup> and the temperatures of 900–1200 °C using thermomechanical simulator Gleeble 3800. The ANN-based model showed high accuracy for both training (the error was 6.6%) and approvement (11.5%) datasets. The values of the effective activation energy for experimental (410 ± 16 kJ/mol) and predicted peak stress values (380 ± 29 kJ/mol) are in good agreement. The implementation of the constructed ANN-based model showed a significant influence of the Cr12Ni3Cu chemical composition variation within the grade on the flow stress at a steady state of the hot deformation.https://www.mdpi.com/2075-4701/12/3/447hot deformationartificial neural networkthermomechanical simulator gleebleCr12Ni3Cu steelconstitutive model |
| spellingShingle | Alexander Churyumov Alena Kazakova Tatiana Churyumova Modelling of the Steel High-Temperature Deformation Behaviour Using Artificial Neural Network Metals hot deformation artificial neural network thermomechanical simulator gleeble Cr12Ni3Cu steel constitutive model |
| title | Modelling of the Steel High-Temperature Deformation Behaviour Using Artificial Neural Network |
| title_full | Modelling of the Steel High-Temperature Deformation Behaviour Using Artificial Neural Network |
| title_fullStr | Modelling of the Steel High-Temperature Deformation Behaviour Using Artificial Neural Network |
| title_full_unstemmed | Modelling of the Steel High-Temperature Deformation Behaviour Using Artificial Neural Network |
| title_short | Modelling of the Steel High-Temperature Deformation Behaviour Using Artificial Neural Network |
| title_sort | modelling of the steel high temperature deformation behaviour using artificial neural network |
| topic | hot deformation artificial neural network thermomechanical simulator gleeble Cr12Ni3Cu steel constitutive model |
| url | https://www.mdpi.com/2075-4701/12/3/447 |
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