Prediction of the mechanical properties of the post-forged Ti-6Al-4V alloy using fuzzy neural network

Isothermal compression of the Ti-6Al-4V alloy was conducted at a 2500. ton isothermal hydrostatic press, and the mechanical properties including ultimate tensile strength, yield strength, elongation and area reduction of the post-forged Ti-6Al-4V alloy were measured at a ZWICK/Z150 testing machine. A fuzzy neural network (FNN) was applied to acquire the relationships between the mechanical properties and the processing parameters of post-forged Ti-6Al-4V alloy. In establishing those relationships, the forging temperature, strain and strain rate were taken as the inputs, whilst the ultimate tensile strength, yield strength, elongation and area reduction were taken as the output respectively. The predicted results using the present FNN model is in a good agreement with the experimental data of the post-forged Ti-6Al-4V alloy, and the optimum processing parameters can be quickly and conveniently selected to achieve the desired mechanical properties by means of the prediction based on the fuzzy neural network model.