Machine-learning-enabled composition–process co-design of heat-resistant cast aluminum alloys with superior elevated-temperature strength

To address the reliance on trial-and-error methods and the prolonged development cycles inherent in the composition and process design of novel cast aluminum alloys, this study constructed a multi-source feature system integrating alloy composition, physicochemical properties of elements, testing conditions, and process parameters. Employing a three-step feature selection method, a prediction model for high-temperature ultimate tensile strength (UTS) was established with a test set R² of 0.881 and an MAE of 22.639 MPa. Based on this model, a synergistic design of the alloy composition and heat treatment process was conducted by coupling the model with a genetic algorithm (GA), and four novel cast aluminum alloys were experimentally validated. The experimental results indicate that the designed alloys exhibit enhanced elevated-temperature strength compared with the commercial reference alloys. Notably, the ZL-2 alloy demonstrated optimal performance, achieving a UTS of 214.2 MPa when tested at 300 °C after holding at 300 °C for 1 h. Furthermore, SHAP (SHapley Additive exPlanations) analysis revealed significant nonlinear interactions among alloy composition, testing conditions, and process parameters in determining tensile strength. Multiscale microstructural characterization reveals that the exceptional elevated-temperature strength of ZL-2 stems from the synergistic effects of nanoscale Al₂₀Cu₂Mn₃ and Al₂CuMg precipitates, and micron-scale Al₃Ti-containing intermetallics. This study demonstrates the application potential of data-driven methods in the composition-process synergistic design of heat-resistant cast aluminum alloys.