Intelligent Material Modelling for Mild Steel Hot Working

In this paper, neural network based constitutive models relating stress to deformation conditions of mild steel subjected to hot forging is aempted through a parallel distributed processing paradigm based on artificial neural network prediction of the metal material response. Laboratory data of the stress-strain behaviour of the mild steel subjected to compression tests with different temperature and strain rate conditions were utilized to evaluate different feed-forward back-propagation neural network models for flow stress prediction. The results obtained displayed a good agreement with the experimental data, showing that the neural network approach can accurately describe the material flow stress under the considered processing condions.