Prediction of flow stress of BT20 titanium alloy by BP neural network

To establish the prediction model of flow stress of BT20 titanium alloy(Ti6Al2Zr1Mo1V),the flow stress curves have been obtained by hot compression experiments and the algorithm of BP neural network has been improved,based on which the flow stress of BT20 alloy has been precisely predicted.The investigation results show that artificial neural networks(ANNs)can predict flow stress without considering material characteristic,which effectively avoids the error caused by some hypotheses and simplifications of conventional empirical or regressive constitutive models.Because ANNs have strong ability in treating nonlinear discrete data,satisfactory prediction precision can be reached on condition that proper network models(mainly including the number of hide layers and neurons)are selected and enough sample data are input to train neural networks.The improved BP neural networks with the network structure of 3×16×14×1 in this paper can predict flow stress of BT20 alloy quite accurately with high calculation efficiency and the prediction model may be used as constitutive relationship for FEM simulation of BT20 alloy during plastic forming.