Application of constitutive and neural network models for prediction of high temperature flow behavior of Al/Mg based nanocomposite

To predicate the high temperature flow behavior of Al/Mg based nanocomposite, constitutive models such as general flow, Arrhenius hyperbolic, Johnson-Cook(JC) and modified Zerilli-Armstrong (ZA) models, and artificial neural network(ANN) models were developed using stress-strain data collected from hot compression tests carried at different strain rates (0.01-1.0 s−1) and temperatures (523, 623 and 723 K). The validity of the models developed was tested using statistical parameters such as root mean square error (RMSE), regression coefficient (R2), mean relative error (MRE) and scattered index (Is). A comparison between ANN and different constitutive models shows that the ANN model has a higher accuracy in estimating the flow stress during hot deformation of AA5083/2%TiC nanocomposite.