A surrogate model-based co-optimization methodology for die-casting process and cooling system

Abstract

In low-pressure die casting (LPDC), there is a complicated relationship between the solidification behavior of the casting and the thermal stress of the mold. Conventional studies consider these two aspects separately, complicating global co-optimization. To address this, the present paper puts forward a methodology for the design of the process parameters and cooling system that is based on surrogate modelling and multi-objective inverse optimization. The LPDC of an aluminum alloy wheel hub was selected as a case study. Samples of process parameters and cooling structure parameters were obtained via optimal Latin hypercube design (OLHD). A thermo–mechanical coupled simulation was performed using ProCAST and Abaqus, resulting in a dataset for casting solidification time and maximum mold thermal stress. Within the framework of Bayesian optimization (BO), the predictive performance of three surrogate models, namely Support Vector Regression (SVR), Kriging, and Extreme Gradient Boosting (XGBoost), was compared. The study shows that BO-XGBoost exhibits lower predictive accuracy than the other two models, while BO-Kriging is marginally better for solidification time and BO-SVR is slightly superior for thermal stress. In addition, a comparative analysis was conducted on the performance of three multi-objective optimization algorithms, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-objective Particle Swarm Optimization (MOPSO), and Logistic Chaotic Mapping-based Sparrow Search Algorithm (LCSSA). The results show that LCSSA demonstrates optimal performance in terms of convergence, distribution, and stability. Consequently, it was selected to perform multi-objective optimization of solidification time and thermal stress. Combined with the surrogate models, an inverse optimization strategy was then employed to extract stable process parameter windows for various scenarios. Simulation verification demonstrates that the recommended parameter intervals satisfy target constraints. This study proposes a systematic methodology for the co-optimization of LPDC processes and mold design, enhancing the engineering applicability and stability of the process under variable working conditions.