Modelling Predication of Flow Stress and Grain Size in the High Temperature Deformation of Ti-6Al-2Zr-2Sn-2Mo- 1.5Cr-2Nb Alloy

A Pi-sigma fuzzy neural network (FNN), in which the layers of neural networks were organized into a feed-forward system, was used to predict the flow stress and the grain size during isothermal compression of Ti-6Al-2Zr-2Sn-2Mo-1.5Cr-2Nb alloy. After the optical micrography (OM) and scanning electron microscopy (SEM) observations, the grain size of primary α phase was measured via a quantitative metallography image analysis software. The effect of deformation temperature and strain rate on the microstructure was discussed. The comparisons of the predicted flow stress and grain size for the sample data or the non-sample data with the experimental results were given to train the models and confirm the validity in present study. The results show that the accuracy of prediction from the Pi-sigma FNN models is much high, and the Pi-sigma FNN approach can efficiently describe the non-linear and complex relationship of titanium alloys.