A Comparative Study of Strain Rate Constitutive and Machine Learning Models for Flow Behavior of AZ31-0.5 Ca Mg Alloy during Hot Deformation
In this study, isothermal compression tests of highly ductile AZ31-0.5Ca Mg alloys were conducted at different strain rates (0.001–0.1 s<sup>−1</sup>) and temperatures (423–523 K) along with extruded direction. The flow stress characteristics were evaluated at elevated temperatures. In a...
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| Format: | Article |
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MDPI AG
2022-02-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/5/766 |
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| author | Umer Masood Chaudry Russlan Jaafreh Abdul Malik Tea-Sung Jun Kotiba Hamad Tamer Abuhmed |
| author_facet | Umer Masood Chaudry Russlan Jaafreh Abdul Malik Tea-Sung Jun Kotiba Hamad Tamer Abuhmed |
| author_sort | Umer Masood Chaudry |
| collection | DOAJ |
| description | In this study, isothermal compression tests of highly ductile AZ31-0.5Ca Mg alloys were conducted at different strain rates (0.001–0.1 s<sup>−1</sup>) and temperatures (423–523 K) along with extruded direction. The flow stress characteristics were evaluated at elevated temperatures. In addition, a strain-dependent constitutive model based on the Arrhenius equation and machine learning (ML) were constructed to evaluate the stress–strain flow behavior. To build the ML model, experimental data containing temperature, strain, and strain rate were used to train various ML algorithms. The results show that under lower temperatures and higher strain rates, the curves exhibited strain hardening, which is due to the higher activation energy, while when increasing the temperature at a fixed strain rate, the strain hardening decreased and curves were divided into two regimes. In the first regime, a slight increase in strain hardening occurred, while in the second regime, dynamic recrystallization and dynamic recovery controlled the deformation mechanism. Our ML results demonstrate that the ML model outperformed the strain-dependent constitutive model. |
| first_indexed | 2024-03-09T20:31:11Z |
| format | Article |
| id | doaj.art-8a15683854544736a05fbb088201473e |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T20:31:11Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-8a15683854544736a05fbb088201473e2023-11-23T23:23:19ZengMDPI AGMathematics2227-73902022-02-0110576610.3390/math10050766A Comparative Study of Strain Rate Constitutive and Machine Learning Models for Flow Behavior of AZ31-0.5 Ca Mg Alloy during Hot DeformationUmer Masood Chaudry0Russlan Jaafreh1Abdul Malik2Tea-Sung Jun3Kotiba Hamad4Tamer Abuhmed5Department of Mechanical Engineering, Incheon National University, Incheon 22012, KoreaSchool of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon 16419, KoreaSchool of Mechanical Engineering, Dongguan University of Technology, Dongguan 523808, ChinaDepartment of Mechanical Engineering, Incheon National University, Incheon 22012, KoreaSchool of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon 16419, KoreaCollege of Computing and Informatics, Sungkyunkwan University, Suwon 16419, KoreaIn this study, isothermal compression tests of highly ductile AZ31-0.5Ca Mg alloys were conducted at different strain rates (0.001–0.1 s<sup>−1</sup>) and temperatures (423–523 K) along with extruded direction. The flow stress characteristics were evaluated at elevated temperatures. In addition, a strain-dependent constitutive model based on the Arrhenius equation and machine learning (ML) were constructed to evaluate the stress–strain flow behavior. To build the ML model, experimental data containing temperature, strain, and strain rate were used to train various ML algorithms. The results show that under lower temperatures and higher strain rates, the curves exhibited strain hardening, which is due to the higher activation energy, while when increasing the temperature at a fixed strain rate, the strain hardening decreased and curves were divided into two regimes. In the first regime, a slight increase in strain hardening occurred, while in the second regime, dynamic recrystallization and dynamic recovery controlled the deformation mechanism. Our ML results demonstrate that the ML model outperformed the strain-dependent constitutive model.https://www.mdpi.com/2227-7390/10/5/766hot compressionflow characteristicsconstitutive analysismachine learning model |
| spellingShingle | Umer Masood Chaudry Russlan Jaafreh Abdul Malik Tea-Sung Jun Kotiba Hamad Tamer Abuhmed A Comparative Study of Strain Rate Constitutive and Machine Learning Models for Flow Behavior of AZ31-0.5 Ca Mg Alloy during Hot Deformation Mathematics hot compression flow characteristics constitutive analysis machine learning model |
| title | A Comparative Study of Strain Rate Constitutive and Machine Learning Models for Flow Behavior of AZ31-0.5 Ca Mg Alloy during Hot Deformation |
| title_full | A Comparative Study of Strain Rate Constitutive and Machine Learning Models for Flow Behavior of AZ31-0.5 Ca Mg Alloy during Hot Deformation |
| title_fullStr | A Comparative Study of Strain Rate Constitutive and Machine Learning Models for Flow Behavior of AZ31-0.5 Ca Mg Alloy during Hot Deformation |
| title_full_unstemmed | A Comparative Study of Strain Rate Constitutive and Machine Learning Models for Flow Behavior of AZ31-0.5 Ca Mg Alloy during Hot Deformation |
| title_short | A Comparative Study of Strain Rate Constitutive and Machine Learning Models for Flow Behavior of AZ31-0.5 Ca Mg Alloy during Hot Deformation |
| title_sort | comparative study of strain rate constitutive and machine learning models for flow behavior of az31 0 5 ca mg alloy during hot deformation |
| topic | hot compression flow characteristics constitutive analysis machine learning model |
| url | https://www.mdpi.com/2227-7390/10/5/766 |
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