Enhancing constitutive description and workability characterization of Mg alloy during hot deformation using machine learning-based Arrhenius-type model  

作　　者：Jinchuan Long Lei Deng Junsong Jin Mao Zhang Xuefeng Tang Pan Gong Xinyun Wang Gangfeng Xiao Qinxiang Xia 

机构地区：[1]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,China [2]School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510640,China

出　　处：《Journal of Magnesium and Alloys》2024年第7期3003-3023,共21页镁合金学报（英文）

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.52305361,51775194,52090043);China Postdoctoral Science Foundation(2023M741245);the National Key Research and Development Program of China(2022YFB3706903).

摘　　要：Hot deformation is a commonly employed processing technique to enhance the ductility and workability of Mg alloy.However,the hot deformation of Mg alloy is highly sensitive to factors such as temperature,strain rate,and strain,leading to complex flow behavior and an exceptionally narrow processing window for Mg alloy.To overcome the shortcomings of the conventional Arrhenius-type(AT)model,this study developed machine learning-based Arrhenius-type(ML-AT)models by combining the genetic algorithm(GA),particle swarm optimization(PSO),and artificial neural network(ANN).Results indicated that when describing the flow behavior of the AQ80 alloy,the PSO-ANN-AT model demonstrates the most prominent prediction accuracy and generalization ability among all ML-AT and AT models.Moreover,an activation energy-processing(AEP)map was established using the reconstructed flow stress and activation energy fields based on the PSO-ANN-AT model.Experimental validations revealed that this AEP map exhibits superior predictive capability for microstructure evolution compared to the one established by the traditional interpolation methods,ultimately contributing to the precise determination of the optimum processing window.These findings provide fresh insights into the accurate constitutive description and workability characterization of Mg alloy during hot deformation.

关 键 词：Constitutive description Workability characterization Machine learning Mg alloy Hot deformation 

分 类 号：TG146.22[一般工业技术—材料科学与工程] TP181[金属学及工艺—金属材料]