Evaluating yield strength of Ni-based superalloys via high throughput experiment and machine learning

Abstract

Yield strength (YS) is a key factor during design and application of Ni-based superalloys with complex compositions, hence it is of great significance to evaluate the YS prior to manufacturing. In this work, alloy diffusion-multiple technology was employed as a high-throughput way to yield the hardness dataset. Based on the composition and other descriptors, Pearson correlation coefficients, stability selection and feature importance were used to select the efficient feature variables. Thereafter, six different machine learning models were applied to predict the YS. Finally, the individual and interaction effect of Co and Mo could be effectively detected by the Gaussian process regression (GPR) model. The optimum composition of Ni-based superalloys with the largest YS at room temperature was determined using the trained GPR model and genetic algorithm. This method can be extended to predict the YS in other multicomponent alloys, such as Ti alloys, Co-based alloys, and high entropy alloys.