XU Z C, GAO S T, CAO Z X, et al． Constitutive model modification and hot deformation behavior of rare earth magnesium alloy based on machine learning［J］. Journal of Henan Polytechnic University( Natural Science)， doi: 10.16186/j.cnki.1673-9787. 

（ 2025030017）..

doi: 10.16186/j.cnki.1673-9787. （2025030017）..

Received： 2025-03-08

Revised： 2025-05-21

Online： 2026-06-04

Constitutive model modification and hot deformation behavior of rare earth magnesium alloy based on machine learning

Xu Zhichao1, Gao Songtao1, Cao Zengxu1, Xiong Feng1, Wang Di2, Yang Wenju3

1. Department of Material Science & Engineering Henan Polytechnic University, Jiaozuo 454003, Henan, China; 2. Center of Energy Economy Study, School of Business Administration, Henan Polytechnic University, Jiaozuo 454003, Henan, China;3. Department of Material Science & Engineering, Zhengzhou university, Zhengzhou 450000, Henan, China

Abstract: Objectives To address the limitations of the Johnson-Cook (JC) constitutive equation in predicting complex nonlinear flow behavior, this study investigates the effects of grain size and texture on the hot workability and deformation mechanisms of LPSO-containing rare earth magnesium alloys. Methods The high-temperature dynamic flow behavior of LPSO-containing Mg-Zn-Y alloys was systematically studied through hot compression experiments. By integrating machine learning techniques with traditional physical models, an artificial neural network-assisted Johnson-Cook model (ANN-JC) incorporating proportional coefficients was proposed. Furthermore, the microstructural characteristics and evolution mechanisms of Mg-Zn-Y alloys during hot deformation were examined in depth. Results The evaluation results of model accuracy indicate that, compared to the traditional JC model, the RMSE and MAPE of the ANN model are reduced by 59.36% and 48.93%, respectively. The proposed ANN-JC model successfully integrates the theoretical foundation of the classical constitutive model with the nonlinear fitting capability of machine learning. This hybrid approach effectively compensates for errors arising from varying softening mechanisms during thermal deformation, thereby significantly enhancing the prediction accuracy and generalization capability for the rheological behavior of materials. As the deformation temperature increases, the degree of dynamic recrystallization (DRX) in Mg-Zn-Y alloys is significantly enhanced. Within unrecrystallized grains, the absorption and evolution of low-angle grain boundaries (LAGBs) gradually form high-angle grain boundaries (HAGBs), promoting the nucleation of DRX grains. With increasing deformation temperature, the occurrence of DRX results in grain refinement and a reduction in texture intensity, thereby weakening the alloy's anisotropic behavior. However, when the deformation temperature rises to 450°C, texture intensity increases again. This phenomenon is primarily attributed to the saturation of the grain refinement effect induced by DRX at high temperatures, coupled with grain growth, which reduces misorientation between grains and aligns their orientations, thereby reinforcing the alloy's texture intensity.

Key words: Rare earth magnesium alloy; Thermal deformation; Constitutive model; Machine Learning; Texture