Steel Phase Kinetics

Steel phase kinetics research focuses on understanding and predicting the transformations between different steel phases (e.g., ferrite, pearlite, bainite, martensite) during heating and cooling, crucial for controlling material properties. Current research employs advanced techniques like deep learning for automated microstructure analysis, enabling objective and rapid quality control by classifying microstructures and quantifying features such as carbide distribution. Furthermore, symbolic regression and data-driven approaches are being developed to create accurate and efficient models of phase transformations, improving the predictability of steel processing and material performance. These advancements offer significant potential for optimizing steel manufacturing processes and enhancing material design.