Design of high performance Cu-Ni-Si alloys via a multiobjective strategy based on machine learning

Herein, the ultimate tensile strength and electrical conductivity of precipitation-strengthened Cu-Ni-Si alloys were simultaneously improved by utilizing a machine learning-based multiobjective design strategy. The multiobjective design strategy consists of five main steps: creating the initial dataset, generating alloy features, screening key alloy features, modeling and inversely designing, and experimental iteration. Of particular note is the constraint placed on the initial composition-properties dataset, considering the rules governing the addition of Co. This constraint ensures that the dataset adheres to the required specifications. To evaluate the optimized degree of the inverse design composition, a joint expectation improvement function was employed. This function effectively integrates the ultimate tensile strength and electrical conductivity. Through a process of five mutually reinforcing iterations of machine learning and experimentation, the combined property of the designed alloy surpasses the Pareto frontier initially formed by the collected data. Microstructure analysis further confirmed the significant precipitation strengthening effects achieved in the optimized alloy.