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

作者：Wenjing Li, Yehang Chen, Xiufang Gong, Jiaqi Wu, Hu Wang, Ying Liu, Lixian Lian · 发表于：Materials Genome Engineering Advances · 年份：2025 · DOI：10.1002/mgea.70026 · 被引用次数：2 · 研究领域：High Temperature Alloys and Creep、Metallurgy and Material Forming、Microstructure and Mechanical Properties of Steels

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.