Application of Gaussian Process regression models for capturing the evolution of microstructure statistics in aging of Nickel-based superalloys

Nickel-based superalloys, used extensively in advanced gas turbine engines, exhibit complex microstructures that evolve during exposure to high temperatures (i.e., aging treatments). In this work, we examine critically if the principal component (PC) representation of rotationally invariant 2-point spatial correlations can adequately capture the salient features of the microstructure evolution in the thermal aging of the superalloys. For this purpose, an experimental study involving microstructure characterization of 27 differently aged (i.e., different combinations of temperature and time of exposure) samples was designed and conducted. Of these, 23 samples were employed for training a Gaussian Process Regression (GPR) model that took the aging temperature and the aging time as inputs, and predicted the microstructure statistics as output. The viability of the approach described above was evaluated critically by comparing the predictions for the four samples that were not used in the training of the GPR model. Furthermore, a new strategy was developed and implemented to generate digital microstructures corresponding to the predicted microstructure statistics. The predicted microstructures were found to be in good agreement with the experimentally measured one, validating the novel framework presented in this work.