A Comparative Study of Strain Rate Constitutive and Machine Learning Models for Flow Behavior of AZ31-0.5 Ca Mg Alloy during Hot Deformation

Umer Masood Chaudry, Russlan Jaafreh, Abdul Malik, Tea-Sung Jun, Kotiba Hamad and Tamer Abuhmed
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Umer Masood Chaudry: Department of Mechanical Engineering, Incheon National University, Incheon 22012, Korea
Russlan Jaafreh: School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon 16419, Korea
Abdul Malik: School of Mechanical Engineering, Dongguan University of Technology, Dongguan 523808, China
Tea-Sung Jun: Department of Mechanical Engineering, Incheon National University, Incheon 22012, Korea
Kotiba Hamad: School of Advanced Materials Science & Engineering, Sungkyunkwan University, Suwon 16419, Korea
Tamer Abuhmed: College of Computing and Informatics, Sungkyunkwan University, Suwon 16419, Korea

Mathematics, 2022, vol. 10, issue 5, 1-13

Abstract: 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 −1 ) 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.

Keywords: hot compression; flow characteristics; constitutive analysis; machine learning model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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