Modelling the quantitative correlation between the microstructure and mechanical properties in titanium alloys

Modelling has now become a routine part of materials science. A model is developed for quantitative analysis and prediction of the correlation between the microstructure and mechanical properties in titanium alloys through the use of soft computing, based on quantifying microstructural features in titanium alloys. The input parameters of the model are the microstructural feature parameters, and the output parameters are some mechanical properties of the alloys. To achieve a good performance of the model, the input microstructural feature parameters must be chosen when designing model in terms of three rules: the qualitative effect of the microstructure on mechanical properties, correlative work processing and the operational efficiency of the model. A model for the analysis and prediction of the correlation between the microstructure and room-temperature tensile properties in Ti-17 titanium alloy is developed by applying artificial neural network, which is given as an example to discuss the modelling methods and approach. The model achieves the desirable predicting performance, so the modelling principle is correct and its implementation is feasible.