Machine Learning Steel Ms Temperature. (June 2021) - Zhang, Yun; Xu, Xiaojie
Empirical equations, thermodynamics frameworks, and neural network modeling have been developed to predict steel martensite start temperature, M s, but they might not tend to generalize well when composition includes a wide range of alloying elements. In this study, we develop the Gaussian process regression (GPR) model to shed light on the relationship between alloying elements andM s temperature for steels. A total of 1119 steels withM s ranging from 153 K to 938 K are examined. The model has a high degree of accuracy and stability, contributing to fast low-costM s temperature estimations.