Data-Driven Design of HPDC Aluminum Alloys Using Machine Learning and Inverse Design

This work proposes a data-driven design framework for high-pressure die-cast (HPDC) aluminum alloys that integrates robust data refinement, machine learning (ML) modeling, explainability, and inverse design. A total of 1237 tensile-test records from T5-aged HPDC alloys were aggregated into a curated dataset of 382 unique composition–heat-treatment combinations. Four regression models—Ridge regression, Random Forest (RF), XGBoost (XGB), and a multilayer perceptron (MLP)—were trained to predict yield strength (YS), ultimate tensile strength (UTS), and elongation (EL). Tree-based ensemble models (XGB and RF) achieved the highest accuracy and stability, capturing nonlinear interactions inherent to industrial HPDC data. In particular, the XGB model exhibited the best predictive performance, achieving test R2 values of 0.819 for UTS and 0.936 for EL, with corresponding RMSE values of 15.23 MPa and 1.112%, respectively. Feature-importance and SHapley Additive exPlanations (SHAP) analyses identified Mn, Si, Mg, Zn, and T5 aging temperature as the most influential variables, consistent with metallurgical considerations such as microstructural stabilization and precipitation strengthening. Finally, RF-based inverse design suggested new composition–process candidates satisfying UTS > 300 MPa and EL > 8%, a region scarcely represented in the experimental dataset. These results illustrate how interpretable ML can expand the feasible design space of HPDC aluminum alloys and support composition–process optimization in industrial applications.