Modeling of hot deformation behavior and prediction of flow stress in a magnesium alloy using constitutive equation and artificial neural network (ANN) model

The aim of the present study was to investigate the modeling and prediction of the high temperature flow characteristics of a cast magnesium (Mg–Al–Ca) alloy by both constitutive equation and ANN model. Toward this end, hot compression experiments were performed in 250–450 °C and in strain rates of 0.001–1 s−1. The true stress of alloy was first and foremost described by the hyperbolic sine function in an Arrhenius-type of constitutive equation taking the effects of strain, strain rate and temperature into account. Predictions indicated that unlike low strain rates and high temperature with dominant DRX activation, in relatively high strain rate and low temperature values, the precision of the models become decreased due to activation of twinning phenomenon. At that moment and for a better evaluation of twinning effect during deformation, a feed-forward back propagation ANN was developed to study the flow behavior of the investigated alloy. Then, the performance of the two suggested models has been assessed using a statistical criterion. The comparative assessment of the gained results specifies that the well-trained ANN is much more precise and accurate than the constitutive equations in predicting the hot flow behavior.