University of Cambridge

Assembly Line

Machine learning predictions of superalloy microstructure

Date:

Authors: Patrick L Taylor, Gareth Conduit

Topics: machine learning, materials science

Organizations: University of Cambridge, Intellegens

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. Additionally, the model predicts the phase composition with uncertainties unlike the traditional CALPHAD method.

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