Multi‐objective optimization of three mechanical properties of Mg alloys through machine learning

Abstract

Conventional trial‐and‐error method is usually time‐consuming and expensive for multi‐objective optimization of Mg alloys. Although machine learning exhibits great potential to accelerate related research studies, machine learning prediction of properties of Mg alloys is often a prediction of a single target at a time. To address this, this paper integrates non‐dominated sorting genetic algorithm III multi‐objective optimization algorithm with light gradient boosting machine algorithm to simultaneously optimize yield strength, ultimate tensile strength, and elongation of Mg alloys. This is the first time that simultaneous machine learning optimization of these three objectives has been achieved for Mg alloys.