ABSTRACT For sustainable alloy design, unified‐composition approaches offer an effective route to deliver multiple performance levels while reducing chemistry complexity. Quenching and partitioning (Q&P) steels are widely used advanced high‐strength steels, yet their grade development typically relies on distinct, grade‐specific chemistries, complicating welding and hindering efficient recycling. This study proposes a physics‐informed machine learning framework for unified‐composition Q&P steel design, enabling multiple strength grades from a single alloy via heat‐treatment tuning. A physics‐guided property‐bridging model integrates metallurgical descriptors with near‐high‐throughput hardness data, transferring knowledge from hardness to tensile properties under sparse tensile labels. This approach enables improved prediction of tensile strength and elongation from limited tensile datasets. A multi‐objective genetic algorithm then explores the composition‐process space to identify one alloy that meets the Q&P980, Q&P1180, and Q&P1380 grade targets via processing adjustments. Compared to a purely data‐driven baseline, the framework substantially improves tensile prediction accuracy (R 2 up to 84% vs. 74%) while maintaining better model stability under data‐sparse conditions. An experimental validation demonstrates that the same chemistry can achieve ∼980, ∼1180, and ∼1380 MPa tensile strengths with suitable ductility under different Q&P schedules. Overall, the physics‐informed framework exemplifies a paradigm for sustainable alloy design that reduces chemistry variants while simplifying recycling.
Wei et al. (Mon,) studied this question.