Development of Neural Networks for the Prediction of the Interrelationship Between Microstructure and Properties of Ti Alloys

Ti alloys possess a rich set of microstructural features that exist over a wide range of size scales. Their interdependent nature has prevented controlled experiments from being undertaken with the aim of identifying the functional dependences of microstructure on properties, and hence there exist no phenomenological relationships to permit the assessment of microstructure/property relationships. Therefore, in the present study, an approach involving Bayesian neural networks has been adopted. The optimized networks have been used to predict, within the ranges of the databases, the interrelationships between microstructure and tensile properties and fracture toughness, generally providing accuracies ≤ 3%. In addition to providing an interpolative prediction (i.e., with input parameters whose values lie within the ranges of the database used to develop the given neural network) of microstructure/property relationships, importantly these networks have been used to conduct virtual experiments to reveal functional dependencies between properties and the input parameters.