In the present work, a critical analysis of the most-commonly used analytical models and recently introduced ANN-based models was performed to evaluate their predictive accuracy within and outside the experimental interval used to generate them. The high-temperature deformation behavior of a medium carbon steel was studied over a wide range of strains, strain rates, and temperatures using hot compression tests on a Gleeble-3800. The experimental flow curves were modeled using the Johnson–Cook, Modified-Zerilli–Armstrong, Hansel–Spittel, Arrhenius, and PTM models, as well as an ANN model. The mean absolute relative error and root-mean-squared error values were used to quantify the predictive accuracy of the models analyzed. The results indicated that the Johnson–Cook and Modified-Zerilli–Armstrong models had a significant error, while the Hansel–Spittel, PTM, and Arrhenius models were able to predict the behavior of this alloy. The ANN model showed excellent agreement between the predicted and experimental flow curves, with an error of less than 0.62%. To validate the performance, the ability to interpolate and extrapolate the experimental data was also tested. The Hansel–Spittel, PTM, and Arrhenius models showed good interpolation and extrapolation capabilities. However, the ANN model was the most-powerful of all the models.

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.