Machine learning approach to model the microstructure and strength of nickel superalloys

Abstract

Nickel superalloys are a class of materials that find crucial applications in technologies such as jet and gas turbine engines. In order to accelerate the further development of these alloys, this thesis develops machine learning models that can better predict their microstructure and strength. The Gaussian process regression (GPR) models of microstructure are shown to be just as good at interpolation as traditional CALPHAD models, with advantages in speed, retrainability, incorporation of non-equilibrium effects, and the effective inclusion of computational data. By incorporating domain knowledge, it is shown that such GPR models can extrapolate into regions of composition space which include elements unseen during the training process. They can also make accurate predictions for the evolution of microstructure when heat treatments are applied. By making use of domain knowledge, similar extrapolations are possible for models of creep strength. Incorporating the results of the microstructure models leads to models of creep strength that meaningfully reveal underlying physical mechanisms of creep deformation.