Estimation of flow stress behavior of AA5083 using artificial neural networks with regard to dynamic strain ageing effect

In this work, neural networks are used for estimation of flow stress of AA5083 with regard to dynamic strain ageing that occurs in certain deformation conditions and varies flow stress behavior of the metal being deformed. The input variables are selected to be strain rate, temperature and strain and the output value is the flow stress. In the first stage, the appearance and terminal of dynamic strain aging are determined with the aid of tensile testing at various temperatures and strain rates and subsequently for the serrated flow and the smooth yielding domains different neural networks are constructed based on the achieved results. While a feed-forward backpropagation algorithm is employed to train the neural networks. Stress–strain curves in both regions are calculated by the employed model and compared with the experimental data. The comparison between the two sets of results indicates the reliability of the predictions.