A comparative study on the phenomenological and artificial neural network models to predict hot deformation behavior of AlCuMgPb alloy

Abstract

The high-temperature deformation behavior of AlCuMgPb alloy was investigated by the hot compression tests over a wide range of deformation temperature (623–773 K) and strain rate (0.005–0.5 s−1). Based on the experimental results, the phenomenological models consist of the Johnson-Cook, Arrhenius-type and Strain-compensation Arrhenius-type constitutive equations and an artificial neural network (ANN) model with a feed forward back propagation learning algorithm were developed for the prediction of the hot deformation behavior of the AlCuMgPb alloy. And then a comparative predictability of the phenomenological models and the trained ANN model were further evaluated in terms of the correlation coefficient(R), average absolute relative error (AARE), root mean square error (RMSE) and relative error. The results showed that the Arrhenius-type constitutive equation could predict the flow stress accurately except under the strain rate of 0.005 s−1. The Strain-compensated Arrhenius-type constitutive equation could represent the elevated temperature flow behavior more accurately than the other investigated phenomenological models consist of Johnson-Cook and Arrhenius-type constitutive equations in the entire processing domain. The results indicated that the trained ANN model is more efficient and accurate in predicting the hot deformation behavior in AlCuMgPb alloy than the investigated phenomenological constitutive equations and offers no physical insight.