Flow behavior of Al–6.2Zn–0.70Mg–0.30Mn–0.17Zr alloy during hot compressive deformation based on Arrhenius and ANN models

Abstract

The hot deformation behavior of Al–6.2Zn–0.70Mg–0.30Mn–0.17Zr alloy was investigated by isothermal compression test on a Gleeble–3500 machine in the deformation temperature range between 623 and 773 K and the strain rate range between 0.01 and 20 s−1. The results show that the flow stress decreases with decreasing strain rate and increasing deformation temperature. Based on the experimental results, Arrhenius constitutive equations and artificial neural network (ANN) model were established to investigate the flow behavior of the alloy. The calculated results show that the influence of strain on material constants can be represented by a 6th-order polynomial function. The ANN model with 16 neurons in hidden layer possesses perfect performance prediction of the flow stress. The predictabilities of the two established models are different. The errors of results calculated by ANN model were more centralized and the mean absolute error corresponding to Arrhenius constitutive equations and ANN model are 3.49% and 1.03%, respectively. In predicting the flow stress of experimental aluminum alloy, the ANN model has a better predictability and greater efficiency than Arrhenius constitutive equations.