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Official PDF TranslationInt. Journal of Minerals, Metallurgy and Materials (矿物冶金与材料学报)

Interpretable machine learning-based stretch formability prediction of magnesium alloys

Authors: Xu Qin; Qinghang Wang; Li Wang; Shouxin Xia; Haowei Zhai; Lingyu Zhao; Ying Zeng; Yan Song; Bin Jiang

DOI: 10.1007/s12613-024-3002-9Status: Verified Translated Edition
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Key Findings in This Report

• Developed an interpretable SSA-XGBoost model achieving high accuracy (R²=0.91) for predicting stretch formability (IE) of AZ31 Mg alloys. • Identified ten key input features from microstructure, mechanical properties, and test conditions, with Imax, TYS, EL, r, GS, and ΔS as most influential. • Validated model generalization with new experimental data, showing prediction errors below 5%. • Provided quantitative insights via SHAP analysis, aiding the design of high-formability magnesium alloys.