中国有色金属学报(英文版)
Transactions of Nonferrous Metals Society of China
| Vol. 36 No. 3 March 2026 |
（a College of Marine Equipment and Mechanical Engineering, Jimei University, Xiamen 361021, China;
b Hunan Engineering Research Center of Forming Technology and Damage Resistance Evaluation for High-Efficiency Light Alloy Components, Hunan University of Science and Technology, Xiangtan 411201, China;
c College of Finance and Economics, Jimei University, Xiamen 361021, China;
d Research Institute of HNU in Chongqing, Hunan University, Chongqing 401135, China;
e School of Materials Science and Engineering, Guangdong Ocean University, Yangjiang 529500, China）
Abstract:To investigate the complex relationship between rolling process parameters and mechanical properties of AZ31 magnesium alloy rolled sheets, the Leave-One-Out Cross-Validation (LOOCV) and parameter tuning were applied to optimizing hyper-parameters for the four (BPNN, SVR, RF, and KNN) machine learning models. An interpretable prediction model based on machine learning and SHapley Additive exPlanations (SHAP), as well as an analytical method combining the SHAP model and the Pearson Correlation Coefficient (PCC), were proposed. The results showed that among the four models, the SVR model was able to simultaneously and accurately predict the ultimate tensile strength (UTS) and elongation (EL). According to the combination analysis of PCC and the magnesium alloy rolling forming mechanism, it was found that strain rate and reduction displayed a negative and positive correlation with UTS, respectively, while rolling temperature and reduction illustrated a positive and negative correlation with EL, respectively. Through the SHAP method, which could interpret the output results of the SVR machine learning model, it was deduced that reduction and strain rate played an important role in the SVR model of the outputs of the UTS and EL, respectively. Combining SHAP with PCC, it was found that strain rate and reduction had a greater influence on the UTS than rolling temperature, whereas strain rate and rolling temperature had more influence on the EL compared to reduction.
Key words: AZ31 magnesium alloy; rolling process; mechanical properties; machine learning; SHapley Additive exPlanations
