Artificial neural network modelling to predict hot deformation behaviour of zinc–aluminium alloy

Abstract

Isothermal hot compression of ZA27 alloy was conducted on a Gleeble-1500 thermomechanical simulator in the temperature range of 473–523 K with strain rates of 0·01–5 s −1 and height reduction of 60. Based on the experimental results, an artificial neural network (ANN) model with a backpropagation learning algorithm was developed for the description and prediction of the hot deformation behaviour. The inputs of the model are temperature, strain rate and strain. The output of the model is the flow stress. Then, a comparative evaluation of the trained ANN model and the constitutive equations was carried out. It was found that the trained ANN model was more efficient and accurate in predicting the hot deformation behaviour of ZA27 alloy.