Machine-Learning-Assisted Composition Design for High-Yield-Strength TWIP Steel

Abstract

Twinning-induced plasticity (TWIP) steel is an ideal material for impact-resistant structures and energy absorption because of its high product of strength and elongation. However, compared with other advanced high-strength steels, the relatively low yield strength of TWIP steel is one of the important shortfalls that significantly limits its engineering applications. To enhance the comprehensive properties of TWIP steel, a machine learning design strategy that integrated comparative modelling, SHAP analysis, and multi-objective optimization were adopted in this study. Initially, various machine learning algorithms were compared for their predictive accuracy based on normalized data (273 entries) regarding the microstructure and properties of TWIP steel. Then, performance prediction models for yield strength, tensile strength, and elongation were established. SHAP analysis was subsequently employed to assess the significance and explicit laws of composition and microstructures in these three target properties, identifying key elements that enhance the overall performance. Furthermore, two new TWIP steels with high yield strengths and high products of strength and elongation were developed via multi-objective optimization. Under conventional hot forging + hot rolling + cold rolling + annealing processes, the two designed TWIP steels had yield strengths of 585 MPa and 560 MPa, tensile strengths of 1055 MPa and 1101 MPa, elongations of 55% and 58.5%, and products of strength and elongation of 58.0 GPa% and 66.4 GPa%, respectively. The yield strengths of the designed TWIP steels significantly improved while maintaining a reasonable product of strength and elongation. This work provides important references for the rational development of new TWIP steels.