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

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Journal of Magnesium and Alloys 2024年第7期12卷 3003-3023页

作者： Jinchuan Long Lei Deng Junsong Jin Mao Zhang Xuefeng Tang Pan Gong Xinyun Wang Gangfeng Xiao Qinxiang Xia State Key Laboratory of Materials Processing and Die&Mould Technology School of Materials Science and EngineeringHuazhong University of Science and Technology1037 Luoyu RoadWuhan 430074China School of Mechanical and Automotive Engineering South China University of TechnologyGuangzhou 510640China

Hot deformation is a commonly employed processing technique to enhance the ductility and workability of Mg ***,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 *** 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 ***,an activation energy-processing(AEP)map was established using the reconstructed flow stress and activation energy fields based on the PSO-ANN-AT *** 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 *** 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

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