Modeling the Quantitative Effect of Alloying Elements on the Ms Temperature of High Carbon Steel by Artificial Neural Networks

Abstract

Chemical composition affects the properties and the martensite start (Ms) temperature of steels. This study predicts the Ms temperature of high carbon steel via artificial neural networks. Meanwhile, it enables us to estimate the quantitative effect of alloying elements on the Ms temperature on a sizeable selectable scale, which is the first time to release such results exactly. Compared to the previous formulas, this one is simple, visual, with high accuracy.