Physical metallurgy guided machine learning to predict hot deformation mechanism of stainless steel

Abstract

This study proposes a novel approach to address the limitations of traditional experimental and mathematical statistical methods in predicting the deformation mechanism of materials. Specifically, a physical metallurgy-guided machine learning method is proposed to achieve fast and accurate prediction of the thermal deformation mechanisms of antibacterial stainless steels with varying Cu contents. First, the Arrhenius and the dynamic recrystallization model were developed utilizing experimental data for the preliminary prediction of the effect of Cu content on the thermal deformation behavior of antibacterial stainless steel. Then, the calculated intermediate parameters were incorporated into the original data set as additional input features to construct a physical metallurgy (PM) guided least squares support vector machine (LSSVM) prediction model, and the optimization of the prediction model is performed through utilization of a particle swarm optimization (PSO) algorithm. The results indicate that the addition of Cu content influences the rheological stress of antibacterial stainless steel at low temperatures and high strain rates. Furthermore, the increase of Cu content reduces the thermal deformation activation energy, inhibiting the dynamic recrystallization of antibacterial stainless steel, while an increase in deformation temperature or a decrease in strain rate promotes recrystallization. The proposed PM-PSO-LSSVM model is in good agreement with experiments, providing theoretical guidance for optimizing the composition and process parameters of antibacterial stainless steel.