Application of artificial neural network for prediction of time–temperature–transformation diagrams in titanium alloys

A model of artificial neural network for simulation of time–temperature–transformation (TTT) diagrams for titanium alloys was designed. Standard backpropagation multilayer feedforward network was created and trained using data from published literature. The influence of aluminium, vanadium, molybdenum and oxygen on transformation kinetics in titanium alloys was assessed on the base of the trained neural network. The results are in good agreement with what is expected from phase transformation theory. Using the model, TTT diagrams for some commercial alloys were predicted. A graphical user interface was created for the use of the model.