Multi-objective optimization for shape memory alloys with large recoverable strain and low hysteresis

Abstract

Design

of shape memory alloys with large phase transformation strain and low hysteresis is in demand for practical applications that require high output work and high precision. However, this remains challenging due to the competition between these two properties. In this work, we report a method that combines machine learning with multi-objective optimization to assist the rapid design of shape memory alloys. Instead of directly using the predictions from machine learning to guide experiments, this work employs the uncertainty-aware two-objective optimization algorithm to recommend the potential candidates. Such a strategy is beneficial to the case where limited data is available just as the dataset of twenty NiTi-based alloys with hysteresis and phase transformation strain established herein. Key features are screened out from a relatively large feature pool and Gaussian regression models are built for predicting the two properties of unknown alloys. At the end, eight alloys with promise to improve both recoverable strain and hysteresis are recommended, as compared to the alloys in the initial dataset.