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The hot deformation behavior of ZA27 alloy was investigated in the temperature range of 473-523 K with the strain rates in the range of 0.01-5 s -1 and the height reduction of 60 % on Gleeble-1500 thermo mechanical simulator. Based on the experimental results, constitutive equations incorporating the effects of temperature, strain rate, and strain have been developed to model the hot deformation behavior of ZA27 alloy. Material constants, a, n, ln A, and activation energy Q in the constitutive equations were calculated as a function of strain. The results showed that the stress-strain curves of ZA27 alloy predicted by the constitutive equations are in good agreement with experimental results, which validates the efficiency of the constitutive equations in describing the hot deformation behavior of the material.

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 21 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.