Target-driven design of high strength yet corrosion resistant medium Mn steel via interpretable machine-learning

作者：Jiayu Wang, Yao Lu, Xiaoya Wang, Siyan Liang, Jie Xiong, Liang Zhen, Li Liu · 发表于：Materials & Design · 年份：2025 · DOI：10.1016/j.matdes.2025.115217 · 被引用次数：6 · 研究领域：Hydrogen embrittlement and corrosion behaviors in metals、Microstructure and Mechanical Properties of Steels、Machine Learning in Materials Science

• An interpretable machine learning framework encompassing data collection, data augmentation, model selection, and experimental validation was proposed to develop novel strong, ductile and corrosion resistant steel. • Shapley additive explanation (SHAP) based analysis identified that austenitization temperature serves as the dominant factor in determining the comprehensive properties of medium Mn steels. • The performance optimized steel possesses a heterogeneous microstructure with both lamellar and block morphology, and block austenite with rare Mn content is more resistant to galvanic corrosion. • The synchronously improved mechanical and corrosion properties of medium Mn steel were attributed to the continuous TRIP effect, moderate corrosion sites, and satisfactory corrosion driving force induced by heterogeneous microstructure. Corrosion-resistant steels with high strength and large ductility are desirable for industrial applications. In this study, an interpretable machine learning (ML) framework including data collection, data augmentation, model selection, and experimental validation was developed for predicting and optimizing the mechanical yet corrosion properties of medium Mn steels. Furthermore, a model interpretation based on Shapley additive explanation (SHAP) approach was utilized to analyze the feature importance. A significant finding in SHAP analysis is that the austenitization temperature serves as the key factor in tailoring mechanical yet corrosion prope...