Composition and processing design of medium-Mn steels based on CALPHAD, SFE modeling, and genetic optimization

Abstract Medium-Mn steels are being thoroughly investigated as potential candidates for the 3rd generation of advanced high strength steels. Despite the wide experimental work, limited attempts have been presented to systematically optimize the chemical composition and heat treatment process to obtain desired microstructural features. In the present work, CALPHAD-based thermodynamic and kinetic modeling, coupled with multi-objective genetic optimization was adopted for the development of δ-ferrite containing medium-Mn steels with optimized microstructure, meeting set design requirements associated with retained austenite fraction and stability. A new sub-regular solution model for the prediction of the austenite stacking fault energy (SFE) was developed and compared to experimental literature data. A MATLAB implementation of the SFE model is provided as supplementary material. Pareto optimal compositions and associated process windows were identified via thermodynamic modeling coupled with the NSGA-II algorithm. A single optimized steel was selected for further consideration through kinetic simulation of the entire process chain including solidification, hot-rolling, accelerated cooling, quenching, and intercritical annealing, considering the effect of δ-ferrite on retained austenite stability and the martensite to austenite transformation kinetics. Temporal optimization resulted in the selection of an optimal intercritical annealing time. Model predictions were validated with metallographic observations on two different δ-ferrite containing medium-Mn steels, revealing excellent agreement between predicted and observed phase fractions.