Flow Stress Models for 40Cr10Si2Mo Steel and Their Application in Numerical Simulation of Hot Forming

Abstract

An accurate flow stress model is crucial in precisely describing material flow behavior and enhancing the precision of hot deformation simulations. Here, the flow stress data of 40Cr10Si2Mo steel were obtained from isothermal compression tests at temperatures of 1173-1398 K and strain rates of 0.01-10 s⁻¹. The flow stress curves were corrected by considering the effect of interfacial friction and deformation heating. These corrected curves were then used to establish the Arrhenius model, back-propagation artificial neural network (BP-ANN) model, and back-propagation artificial neural network optimized by the Harris hawks optimization algorithm (HHO-BP) model. Each flow stress model’s prediction accuracy was assessed using the correlation coefficient (R), average absolute relative error (AARE), and mean square error (MSE). Comparative analysis reveals that the HHO-BP model exhibits the highest precision, as evidenced by its R, MSE, and AARE values of 0.99923, 10.4669, and 1.282%, respectively. Following this, the HHO-BP model was employed to expand the stress–strain data of 40Cr10Si2Mo steel at temperatures of 1210 K, 1285 K, and 1360 K. These expanded data were then used in thermal compression simulations, and high load-stroke simulation accuracy was achieved.