High temperature deformation behavior of Ti-6Al-4V alloy with an equiaxed microstructure: a neural networks analysis

Abstract

The hot deformation behavior of Ti-6Al-4V alloy with an equiaxed microstructure was investigated by means of Artificial Neural Networks (ANN). The flow stress data for the ANN model training was obtained from compression tests performed on a then-no-mechanical simulator over a wide range of temperature (from 700 degrees C to 1100 degrees C) with strain rates of 0.0001 s(-1) to 100 s(-1) and true strains of 0. 1 to 0.6. It was found that the trained neural network could reliably predict flow stress for unseen data. Workability was evaluated by means of processing maps with respect to strain, strain rate, and temperature. Processing maps were constructed at different strains by utilizing the flow stress predicted by the model at finer intervals of strain rates and temperatures. The specimen failures at various instances were predicted and confirmed by experiments. The results establish that artificial neural networks can be effectively used for generating a more reliable processing map for industrial applications. A graphical user interface was designed for ease of use of the model.