| Abstract Scope |
Machine learning is revolutionizing the material design community, especially for high-performing steels. However, emerging methods such as automated laboratories suffer from poor interpretability and requirements for huge amount of data. The current study combines physical metallurgy knowledge and microstructural data to accurately predict properties of steels and improve steel design. Guided by thermodynamic data, deep learning models achieve precise predictions for frictional work and the martensite transformation start temperature. For more complex scenarios such creep and fatigue, transfer learning is employed where source models are established to understand the relationship amongst composition, processing, and mechanical properties. These learned mechanisms are then used to predict fatigue and creep properties. Recognizing that most problems in steel design rely heavily on microstructural information, rapid quantification methods for microstructural images are developed, and multimodal information pertaining to the images is extracted to develop site-specific AI strategies, finally achieving a comprehensive integration of prediction capabilities. |