Phase-field-informed machine learning on creep behavior of Ni-based single-crystal superalloys

Creep strain characterizes the degree of creep damage and the creep life of superalloys. The creep process is accompanied by element redistribution and microstructure evolution; understanding the multi-characteristic relationships of creep morphology and strain/stress is essentially important for the design and prediction of superalloys. Accurate prediction of creep strain necessitates comprehensive feature data. In this work, a phase-field (PF)-informed machine learning (ML) is developed to investigate the creep strain of Ni-12.2Al-6Co-2.5Ta (at.%) superalloy. The creep damage crystal plasticity PF model is employed to simulate the creep morphology, composition and strain evolution. A ML-based quantitative prediction model for creep strain is established to assess the impact of composition and microstructure on creep behavior. Moreover, to enhance the accuracy and generalization of the ML model, statistical features are added using two-point analysis and principal component analysis (PCA) methods for characterizing the two-phase morphology. Additionally, the Shapley Additive Explanations algorithm is used to explain the intrinsic relationships of γ’ rafting, γ’ volume fraction, and creep strain. The phase classification model has an accuracy rate of over 99.2%; the mean square error of the quantitative creep strain prediction model is reduced from 0.304 to 0.235 by using two-point analysis and PCA dimensionality reduction. This study demonstrates the effectiveness of integrating PF information-driven ML in developing image recognition and creep performance prediction models for superalloys.