Machine learning predictions of superalloy microstructure

Abstract

Gaussian process regression machine learning with a physically-informed kernel is used to model the phase compositions of nickel-base superalloys. The model delivers good predictions for laboratory and commercial superalloys, with $R^2>0.8$ for all but two components of each of the $\gamma$ and ${\gamma }^{\prime }$ phases, and $R^2=0.924$ ($\mathrm{RMSE}=0.063$) for the ${\gamma }^{\prime }$ fraction. For four benchmark SX-series alloys the methodology predicts the ${\gamma }^{\prime }$ phase composition with $\mathrm{RMSE}=0.006$ and the fraction with $\mathrm{RMSE}=0.020$, superior to the $0.007$ and $0.021$ respectively from CALPHAD. Furthermore, unlike CALPHAD Gaussian process regression quantifies the uncertainty in predictions, and can be retrained as new data becomes available.