A new creep prediction model in Ni-based superalloys via data-driven symbolic regression

Abstract

In this study, the knowledge-constrained symbolic regression method was used to predict the creep life of Ni-based superalloys. Two forms of prediction formulas that can explain the creep mechanism of Ni-based superalloys were successfully constructed based on high-throughput data-driven approaches combined with machine learning algorithms. Through the selection and calculation of characteristic parameters, the integration factors VγʹTγʹ and 1/Γ were surprisingly found, which indicated the importance of γ′ phase strengthening and dislocation strengthening for the creep. Finally, the models were verified by experimental data, indicating that the prediction effect is excellent. It is notable that the models offer three key advantages: accurate creep life prediction, visual form, and interpretable mechanism.

Keywords

creep life prediction / machine learning / Ni-based superalloy / symbolic regression

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2025 The Author(s). Materials Genome Engineering Advances published by Wiley-VCH GmbH on behalf of University of Science and Technology Beijing.