RF-NSGA-II framework for inverse design of high-performance Mg-Gd-based magnesium alloys

Abstract

An inverse design framework, RF-NSGA-II, is developed using machine learning (ML) methods and a multi-objective co-optimization strategy. It enables intelligent design of chemical compositions and processing parameters for thermo-mechanical treatments based on desired mechanical properties of Mg alloys. Using a database of extruded Mg-Gd and Mg-Y-based alloys, RF-NSGA-II integrates an optimized forward model with a non-dominated sorting genetic algorithm II (NSGA-II). The forward model is constructed by evaluating the performance of different ML algorithms, with the random forest (RF) algorithm experimentally validated to accurately describe the relationship between chemical composition and mechanical properties. RF-NSGA-II simultaneously optimizes multiple mechanical properties, and validation through experimental measurements demonstrates its effectiveness. Using target mechanical properties as inputs, chemical compositions and processing parameters for solid-solution treatment and extrusion are efficiently determined for a high-strength Mg-11.5Gd-6.0Y-1.0Zn-0.2Mn (wt.%) alloy and a high-ductility Mg-2.5Gd-1.0Zn (wt.%) alloy, achieving tensile strength/elongation values of 417 MPa/3.2% and 223 MPa/34%, respectively. These results provide a transparent and effective route for the inverse design of advanced Mg alloys based on desired mechanical properties.

Keywords

Magnesium alloys / machine learning / inverse design / mechanical properties

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