Machine Learning-Based Multi-Objective Composition Optimization of High-Nitrogen Austenitic Stainless Steels

作者：Yinghu Wang, Long Chen, Limei Cheng, Enuo Wang, Zhendong Sheng, Ligang Zhang · 发表于：Materials · 年份：2025 · DOI：10.3390/ma18235460 · 被引用次数：2 · 研究领域：Machine Learning in Materials Science、Hydrogen embrittlement and corrosion behaviors in metals、Microstructure and Mechanical Properties of Steels

High-nitrogen austenitic stainless steels (HNASS) require compositional strategies that simultaneously maximize corrosion resistance and microstructural stability while suppressing delta (δ) ferrite and deleterious precipitates. Here, an explainable multi-objective design workflow is developed that couples thermodynamic descriptors from the Calculation of Phase Diagrams (CALPHAD) approach—using both equilibrium and Scheil solidification calculations—with machine learning surrogate models, random forest (RF) and Extreme Gradient Boosting (XGBoost), trained on 60,480 compositions in the Fe–C–N–Cr–Mn–Mo–Ni–Si space. The physics-informed feature set comprises phase fractions; transformation and precipitation temperatures for δ-ferrite, chromium nitride (Cr2N), sigma (σ) phase and M23C6 carbides; liquidus and solidus temperatures; and the pitting-resistance equivalent number (PREN). The RF model achieves consistently low prediction errors, with a PREN root-mean-square error (RMSE) of ≈0.004, and exhibits strong generalization. Shapley additive explanations (SHAP) reveal metallurgically consistent trends: increasing nitrogen (N) suppresses δ-ferrite and promotes Cr2N; carbon (C) promotes M23C6; molybdenum (Mo) promotes the σ-phase; and C and silicon (Si) widen the freezing range. Using the trained surrogate as the objective evaluator, the non-dominated sorting genetic algorithm III (NSGA-III) builds Pareto fronts that minimize the δ-ferrite range, Cr2N, σ-phase, M23C6 and the freez...