A Data-Driven Model for Predicting Casting Solidification Time and Mold Thermal Stress

This study proposes a data-driven surrogate modeling framework for predicting solidification time and mold thermal stress during low-pressure die casting (LPDC) of aluminum alloy wheels. The methodology employed an optimal Latin hypercube design (OLHD) to sample key parameters including cooling channel geometry and process conditions. A sequential simulation methodology combining ProCAST and Abaqus was implemented to generate a comprehensive dataset of solidification times and thermal stress distributions. Based on this dataset, surrogate models were developed using Support Vector Regression, Kriging, and Polynomial Response Surface Methodology, with their hyperparameters automatically tuned through Bayesian Optimization (BO). The optimized models were rigorously evaluated using four statistical metrics: Coefficient of Determination (R²), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The evaluation results show that the BO–SVR model demonstrated superior prediction accuracy for both output responses and exhibited exceptional nonlinear fitting capability. This work establishes an effective modeling approach for simultaneous quality and efficiency optimization in wheel manufacturing.