Determination of the influence of processing parameters on the mechanical properties of the Ti–6Al–4V alloy using an artificial neural network

There are many difficulties associated with the development of a quantitative correlation model relating the thermo-mechanical processing parameters to mechanical properties due to the complexity of the problem. In this research, based on the experimental data obtained from a series of forging and heat treatment experiments, the correlation model between hot processing parameters and the mechanical properties of the Ti–6Al–4V alloy has been established using an artificial neural network (ANN) approach. In the proposed model, the input variables are forging temperature, degree of deformation, annealing temperature and annealing time. The mechanical properties are determined as the output variables, including ultimate tensile strength, yield strength, elongation and reduction in area. Subsequently, the generalization capability of the trained ANN model was tested using an unseen data sample. The combined influence of hot processing parameters on the mechanical properties is further studied using the present model. It is found that a reliable correlation between processing parameters and mechanical properties of the Ti–6Al–4V alloy can be obtained. The artificial neural network method is capable of presenting the complex nonlinear relationship including interactions associated with hot processing parameters and mechanical properties.