Abstract
In this paper, a multilayer feedforward neural network with Bayesian regularization
constitutive model is developed for alloy 316L during high strain rate and high temperature
plastic deformation. The input variables are strain rate, temperature and strain while the output
value is the flow stress of the material. The results show that the use of Bayesian regularized
technique reduces the potential of overfitting and overtraining. The prediction quality of the
model is thereby improved. The model predictions are in good agreement with experimental
measurements. The measurement data used for the network training and model comparison
were taken from relevant literature. The developed model is robust as it can be generalized to
deformation conditions slightly below or above the training dataset.