Constructing processing map of Ti40 alloy using artificial neural network

Abstract

Based on the experimental data of Ti40 alloy obtained from Gleeble-1500 thermal simulator, an artificial neural network model of high temperature flow stress as a function of strain, strain rate and temperature was established. In the network model, the input parameters of the model are strain, logarithm strain rate and temperature while flow stress is the output parameter. Multilayer perceptron (MLP) architecture with back-propagation algorithm is utilized. The present study achieves a good performance of the artificial neural network (ANN) model, and the predicted results are in agreement with experimental values. A processing map of Ti40 alloy is obtained with the flow stress predicted by the trained neural network model. The processing map developed by ANN model can efficiently track dynamic recrystallization and flow localization regions of Ti40 alloy during deforming. Subsequently, the safe and instable domains of hot working of Ti40 alloy are identified and validated through microstructural investigations.