Abstract: This study develops a hybrid modeling approach combining the Hall⁃Petch equation with data mining techniques to predict mechanical properties of non⁃oriented electrical steel for new energy vehicle motors. By integrating materials science principles with industrial big data analytics, a high⁃precision prediction system was established. From over 300 production parameters (including chemical composition, process variables, and magnetic properties), key features were selected via bootstrap random forest, followed by principal component analysis (80.5 % cumulative variance) using JMP software. Comparative analysis of multiple algorithms (random forest, KNN, neural networks, and stepwise regression) revealed random forest as the best standalone model (R²=0.987). The Stacking ensemble model, combining random forest and KNN as base learners with GBDT meta⁃learner, achieved superior performance (R²=0.998, RMSE=1.986). Deployed in manufacturing execution systems (MES), this model enables real⁃time mechanical property prediction using online iron loss data, supporting dynamic process optimization.

Key words: mechanical property prediction, machine learning, stacking ensemble model, feature engineering