Artificial Neural Network-Based Critical Conditions for the Dynamic Recrystallization of Medium Carbon Steel and Application
This study presents a novel and thorough approach to comprehending and simulating the DRX process while hot compressing steel. To achieve this goal, we studied the high-temperature deformation behavior of a medium-carbon steel through hot compression testing on a Gleeble-3800 thermomechanical simula...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/13/10/1746 |
| _version_ | 1827720398401175552 |
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| author | Pierre Tize Mha Prashant Dhondapure Mohammad Jahazi Amèvi Tongne Olivier Pantalé |
| author_facet | Pierre Tize Mha Prashant Dhondapure Mohammad Jahazi Amèvi Tongne Olivier Pantalé |
| author_sort | Pierre Tize Mha |
| collection | DOAJ |
| description | This study presents a novel and thorough approach to comprehending and simulating the DRX process while hot compressing steel. To achieve this goal, we studied the high-temperature deformation behavior of a medium-carbon steel through hot compression testing on a Gleeble-3800 thermomechanical simulator over a broad range of strains, strain rates, and temperatures. We also employed an artificial neural network (ANN) to model the thermo-visco-plastic behavior with a flow law. The precision of quantifying the DRX volume fraction is dependent on critical conditions, which are essential for both analytical model evaluation and numerical implementation in finite element software. This study proposes a second ANN, serving as a universal approximator, to fit the data required for DRX critical condition calculations, whereas the Johnson–Mehl–Avrami–Kohnogorov (JMAK) model served as an analytical tool to estimate the DRX volume fraction, which underwent validation through experimental measurements. A numerical implementation of the JMAK model was conducted in ABAQUS software and compared against experimental data by means of microstructure analysis. The comparison revealed a strong correlation between the simulation and experiment. The study investigated the impact of temperature, strain, and strain rate on DRX evolution. The findings showed that DRX increases with rising temperature and strain but decreases with increasing strain rate. |
| first_indexed | 2024-03-10T21:02:44Z |
| format | Article |
| id | doaj.art-8f796a98f05f43c49db47a3b53aa5413 |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-10T21:02:44Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-8f796a98f05f43c49db47a3b53aa54132023-11-19T17:22:22ZengMDPI AGMetals2075-47012023-10-011310174610.3390/met13101746Artificial Neural Network-Based Critical Conditions for the Dynamic Recrystallization of Medium Carbon Steel and ApplicationPierre Tize Mha0Prashant Dhondapure1Mohammad Jahazi2Amèvi Tongne3Olivier Pantalé4Laboratoire Génie de Production, Institut National Polytechnique/Ecole Nationale d’Ingénieurs de Tarbes, Université de Toulouse, 47 Avenue d’Azereix, F-65016 Tarbes, FranceDepartment of Mechanical Engineering, École de Technologie Supérieure, 1100 Notre Dame St. W., Montreal, QC H3C 1K3, CanadaDepartment of Mechanical Engineering, École de Technologie Supérieure, 1100 Notre Dame St. W., Montreal, QC H3C 1K3, CanadaLaboratoire Génie de Production, Institut National Polytechnique/Ecole Nationale d’Ingénieurs de Tarbes, Université de Toulouse, 47 Avenue d’Azereix, F-65016 Tarbes, FranceLaboratoire Génie de Production, Institut National Polytechnique/Ecole Nationale d’Ingénieurs de Tarbes, Université de Toulouse, 47 Avenue d’Azereix, F-65016 Tarbes, FranceThis study presents a novel and thorough approach to comprehending and simulating the DRX process while hot compressing steel. To achieve this goal, we studied the high-temperature deformation behavior of a medium-carbon steel through hot compression testing on a Gleeble-3800 thermomechanical simulator over a broad range of strains, strain rates, and temperatures. We also employed an artificial neural network (ANN) to model the thermo-visco-plastic behavior with a flow law. The precision of quantifying the DRX volume fraction is dependent on critical conditions, which are essential for both analytical model evaluation and numerical implementation in finite element software. This study proposes a second ANN, serving as a universal approximator, to fit the data required for DRX critical condition calculations, whereas the Johnson–Mehl–Avrami–Kohnogorov (JMAK) model served as an analytical tool to estimate the DRX volume fraction, which underwent validation through experimental measurements. A numerical implementation of the JMAK model was conducted in ABAQUS software and compared against experimental data by means of microstructure analysis. The comparison revealed a strong correlation between the simulation and experiment. The study investigated the impact of temperature, strain, and strain rate on DRX evolution. The findings showed that DRX increases with rising temperature and strain but decreases with increasing strain rate.https://www.mdpi.com/2075-4701/13/10/1746artificial neural networkconstitutive flow lawGleeble simulatordynamic recrystallizationfinite element analysis |
| spellingShingle | Pierre Tize Mha Prashant Dhondapure Mohammad Jahazi Amèvi Tongne Olivier Pantalé Artificial Neural Network-Based Critical Conditions for the Dynamic Recrystallization of Medium Carbon Steel and Application Metals artificial neural network constitutive flow law Gleeble simulator dynamic recrystallization finite element analysis |
| title | Artificial Neural Network-Based Critical Conditions for the Dynamic Recrystallization of Medium Carbon Steel and Application |
| title_full | Artificial Neural Network-Based Critical Conditions for the Dynamic Recrystallization of Medium Carbon Steel and Application |
| title_fullStr | Artificial Neural Network-Based Critical Conditions for the Dynamic Recrystallization of Medium Carbon Steel and Application |
| title_full_unstemmed | Artificial Neural Network-Based Critical Conditions for the Dynamic Recrystallization of Medium Carbon Steel and Application |
| title_short | Artificial Neural Network-Based Critical Conditions for the Dynamic Recrystallization of Medium Carbon Steel and Application |
| title_sort | artificial neural network based critical conditions for the dynamic recrystallization of medium carbon steel and application |
| topic | artificial neural network constitutive flow law Gleeble simulator dynamic recrystallization finite element analysis |
| url | https://www.mdpi.com/2075-4701/13/10/1746 |
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