Artificial Neural Network-Based Critical Conditions for the Dynamic Recrystallization of Medium Carbon Steel and Application

Abstract

This study presents a novel and thorough approach to comprehending and simulating the DRX process while hot compressing steel. To achieve this goal, we studied the high-temperature deformation behavior of a medium-carbon steel through hot compression testing on a Gleeble-3800 thermomechanical simulator over a broad range of strains, strain rates, and temperatures. We also employed an artificial neural network (ANN) to model the thermo-visco-plastic behavior with a flow law. The precision of quantifying the DRX volume fraction is dependent on critical conditions, which are essential for both analytical model evaluation and numerical implementation in finite element software. This study proposes a second ANN, serving as a universal approximator, to fit the data required for DRX critical condition calculations, whereas the Johnson–Mehl–Avrami–Kohnogorov (JMAK) model served as an analytical tool to estimate the DRX volume fraction, which underwent validation through experimental measurements. A numerical implementation of the JMAK model was conducted in ABAQUS software and compared against experimental data by means of microstructure analysis. The comparison revealed a strong correlation between the simulation and experiment. The study investigated the impact of temperature, strain, and strain rate on DRX evolution. The findings showed that DRX increases with rising temperature and strain but decreases with increasing strain rate.