Constitutive modeling and workability characterization of pre-deformed AZ31 magnesium alloy during hot shear-compression deformation

Pre-deformation is a cost-efficient method to control the microstructure and mechanical properties of wrought magnesium (Mg) alloys. It introduces twins that promote dynamic recrystallization during hot deformation, thereby improving hot workability and weakening texture. However, uniaxial hot compr...

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Main Authors: Junsong Jin, Fangtao Chai, Jinchuan Long, Chang Gao, Shaolei Wang, Pan Zeng, Xuefeng Tang, Pan Gong, Mao Zhang, Lei Deng, Xinyun Wang
Format: Article
Language:English
Published: Elsevier 2025-07-01
Series:Journal of Materials Research and Technology
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Online Access:http://www.sciencedirect.com/science/article/pii/S2238785425017855
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author Junsong Jin
Fangtao Chai
Jinchuan Long
Chang Gao
Shaolei Wang
Pan Zeng
Xuefeng Tang
Pan Gong
Mao Zhang
Lei Deng
Xinyun Wang
author_facet Junsong Jin
Fangtao Chai
Jinchuan Long
Chang Gao
Shaolei Wang
Pan Zeng
Xuefeng Tang
Pan Gong
Mao Zhang
Lei Deng
Xinyun Wang
author_sort Junsong Jin
collection DOAJ
description Pre-deformation is a cost-efficient method to control the microstructure and mechanical properties of wrought magnesium (Mg) alloys. It introduces twins that promote dynamic recrystallization during hot deformation, thereby improving hot workability and weakening texture. However, uniaxial hot compression tests fail to reflect the deformation behavior of Mg alloys under hot shear-compression process. In this study, shear-compression samples (SCS) were employed, and finite element simulations were combined with physical experiments to determine the Mises coefficients in the equivalent stress-strain conversion equation. The deformation characteristics, flow behavior and microstructure/texture evolution mechanisms of pre-deformed AZ31 alloy were systematically investigated under varying process parameters. A genetic algorithm-optimized artificial neural network (GA-ANN) constitutive model was developed using machine learning methods, and hot processing maps were established based on this model. The results show that the proposed method can accurately determine the Mises coefficients, describing the mechanical response of SCS at high temperatures. The shear-compression state causes the material's flow behavior to exhibit a distinct single-peak characteristic, with peak stress and peak strain increasing and then decreasing with the amount of pre-deformation.The GA-ANN model demonstrates good prediction accuracy and generalization ability for the hot shear-compression behavior of pre-deformed AZ31 alloy. The developed hot processing map can precisely predict microstructure evolution. The optimal pre-deformation amount is determined to be 2 %, with a recommended hot processing window defined as 300–400 °C and 0.07–7 s−1.
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spelling doaj-art-20e55a12b53741be9893067b92cafc192025-08-20T03:51:14ZengElsevierJournal of Materials Research and Technology2238-78542025-07-01375035505210.1016/j.jmrt.2025.07.127Constitutive modeling and workability characterization of pre-deformed AZ31 magnesium alloy during hot shear-compression deformationJunsong Jin0Fangtao Chai1Jinchuan Long2Chang Gao3Shaolei Wang4Pan Zeng5Xuefeng Tang6Pan Gong7Mao Zhang8Lei Deng9Xinyun Wang10State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, China; Corresponding author.State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, ChinaState Key Laboratory of Precision Manufacturing for Extreme Service Performance, School of Mechanical and Electrical Engineering, Central South University, Changsha, 410083, ChinaState Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, ChinaCollege of Weaponry Engineering, Naval University of Engineering, Wuhan, 430033, ChinaState Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, ChinaState Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, ChinaState Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, ChinaState Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, ChinaState Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, ChinaState Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, China; Corresponding author.Pre-deformation is a cost-efficient method to control the microstructure and mechanical properties of wrought magnesium (Mg) alloys. It introduces twins that promote dynamic recrystallization during hot deformation, thereby improving hot workability and weakening texture. However, uniaxial hot compression tests fail to reflect the deformation behavior of Mg alloys under hot shear-compression process. In this study, shear-compression samples (SCS) were employed, and finite element simulations were combined with physical experiments to determine the Mises coefficients in the equivalent stress-strain conversion equation. The deformation characteristics, flow behavior and microstructure/texture evolution mechanisms of pre-deformed AZ31 alloy were systematically investigated under varying process parameters. A genetic algorithm-optimized artificial neural network (GA-ANN) constitutive model was developed using machine learning methods, and hot processing maps were established based on this model. The results show that the proposed method can accurately determine the Mises coefficients, describing the mechanical response of SCS at high temperatures. The shear-compression state causes the material's flow behavior to exhibit a distinct single-peak characteristic, with peak stress and peak strain increasing and then decreasing with the amount of pre-deformation.The GA-ANN model demonstrates good prediction accuracy and generalization ability for the hot shear-compression behavior of pre-deformed AZ31 alloy. The developed hot processing map can precisely predict microstructure evolution. The optimal pre-deformation amount is determined to be 2 %, with a recommended hot processing window defined as 300–400 °C and 0.07–7 s−1.http://www.sciencedirect.com/science/article/pii/S2238785425017855Mg alloyPre-deformationHot shear-compression deformationMachine learningHot workability
spellingShingle Junsong Jin
Fangtao Chai
Jinchuan Long
Chang Gao
Shaolei Wang
Pan Zeng
Xuefeng Tang
Pan Gong
Mao Zhang
Lei Deng
Xinyun Wang
Constitutive modeling and workability characterization of pre-deformed AZ31 magnesium alloy during hot shear-compression deformation
Journal of Materials Research and Technology
Mg alloy
Pre-deformation
Hot shear-compression deformation
Machine learning
Hot workability
title Constitutive modeling and workability characterization of pre-deformed AZ31 magnesium alloy during hot shear-compression deformation
title_full Constitutive modeling and workability characterization of pre-deformed AZ31 magnesium alloy during hot shear-compression deformation
title_fullStr Constitutive modeling and workability characterization of pre-deformed AZ31 magnesium alloy during hot shear-compression deformation
title_full_unstemmed Constitutive modeling and workability characterization of pre-deformed AZ31 magnesium alloy during hot shear-compression deformation
title_short Constitutive modeling and workability characterization of pre-deformed AZ31 magnesium alloy during hot shear-compression deformation
title_sort constitutive modeling and workability characterization of pre deformed az31 magnesium alloy during hot shear compression deformation
topic Mg alloy
Pre-deformation
Hot shear-compression deformation
Machine learning
Hot workability
url http://www.sciencedirect.com/science/article/pii/S2238785425017855
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