Neural network prediction of the effect of thermomechanical controlled processing on mechanical properties

Abstract

The as-rolled mechanical properties of microalloyed steels result from their chemical composition and thermomechanical processing history. Accurate predictions of the mechanical properties would reduce the need for expensive and time-consuming testing. At the same time, understanding the interplay between process variables and alloy composition will help reduce product variability and facilitate future alloy design. This paper provides an artificial neural network methodology to predict lower yield strength (LYS) and ultimate tensile strength (UTS). The proposed method uses feature engineering to transform raw data into features typically used in physical metallurgy to better utilize the artificial neural network model in understanding the process. SHAP values are used to reveal the effect of thermomechanical controlled processing, which can be rationalized by physical metallurgy theory.