Abstract: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 (I_s). 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.

Key words: hot compression; Johnson-Cook (JC) model; Modified Zerilli-Armstrong (ZA) model; Arrhenius (AR) hyperbolic model; flow stress; nanocomposite