Abstract
Artificial neural network is used to model INCONEL 718 in this paper. The
model accounts for precipitate hardening in the alloy. The input variables for the
neural network model are strain, strain rate, temperature and microstructure state. The
output variable is the flow stress. The early stopping technique is combined with
Bayesian regularization process in training the network. Sample and non-sample
measurement data were taken from the literature. The model predictions of flow stress
of the alloy are in good agreement with experimental measurements.