Artificial Neural Network Models for Predicting Flow Stress and Microstructure Evolution of a Hydrogenized Titanium Alloy

摘要

The effects of hydrogen contents and processing parameters of hot deformation on a Ti-6Al-2Zr-1Mo-1V alloy were investigated. Hot compressive tests were conducted at different temperatures and strain rates with various hydrogen contents. Based on these experimental data, the simulation models for predicting flow stress and microstructure evolution have been built by back propagation (BP) neural network. The numerical results gained via the networks were compared with the experimental results.