Modelling the correlation between processing parameters and properties in titanium alloys using artificial neural network

Abstract

A model is developed for the analysis and prediction of the correlation between processing (heat treatment) parameters and mechanical properties in titanium alloys by applying artificial neural network (ANN). The input parameters of the neural network (NN) are alloy composition, heat treatment parameters and work (test) temperature. The outputs of the NN model are nine most important mechanical properties namely ultimate tensile strength, tensile yield strength, elongation, reduction of area, impact strength, hardness, modulus of elasticity, fatigue strength and fracture toughness. The model is based on multilayer feedforward neural network. The NN is trained with comprehensive dataset collected from both the Western and Russian literature. A very good performance of the neural network is achieved. Some explanation of the predicted results from the metallurgical point of view is given. The model can be used for the prediction of properties of titanium alloys at different temperatures as functions of processing parameters and heat treatment cycle. It can also be used for the optimization of processing and heat treatment parameters. Graphical user interface (GUI) is developed for use of the model.