Evolution analysis of γ' precipitate coarsening in Co-based superalloys using kinetic theory and machine learning

The coarsening of γ` precipitates in superalloys involves multiple factors and impacts the performance of mechanical properties, such as strength and creep resistance. Classical kinetic theory describes the coarsening of γ` under limited conditions, as it shows deficiencies in γ` precipitation with excessive coarsening. Finding effective factors underlying the kinetic behavior significantly affecting precipitate coarsening remains largely unsolved. We address this by using machine learning models to identify materials descriptors describing γ` precipitate coarsening in Co-based superalloys. Using descriptors that include Young`s modulus difference and valence electron number, we obtain an explicit relation assisted by symbolic regression that fits the experimental γ` size data far better than classical kinetic theory in describing γ` coarsening. We infer that the Young`s modulus difference dominates γ` coarsening mechanism in different Co-based superalloys. Moreover, alloys containing additional elements with large Young`s modulus should favor smaller γ` precipitates size, thus providing guidance for designing advanced multicomponent Co-based superalloys with high γ` coarsening resistance.