Machine learning predictions of superalloy microstructure

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' phases, and R-2 = 0.924 (RMSE = 0.063) for the gamma' fraction. For four benchmark SX-series alloys the methodology predicts the gamma' phase composition with RMSE = 0.006 and the fraction with 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.

Automated summary

Gaussian process regression machine learning with a physically-informed kernel is used to model the phase compositions of nickel-base superalloys, providing accurate predictions with quantified uncertainty and the ability to be retrained with new data.