Abstract: Aiming at the problems of scarce data, poor dynamic adaptability of models, and difficulty in uncertainty quantification faced in the prediction of high-temperature creep performance of SiC/SiC ceramic matrix composites for aero-engines, this paper proposes a degradation trajectory model integrating the metabolic GM(1,1) model, stochastic process, and grey cloud theory. By dynamically updating the data sequence, the model captures the creep trend in real time, and uses the grey cloud model to quantify the randomness and fuzziness in the creep process, realizing the cloud droplet distribution characterization of the performance degradation range. Case analysis shows that the proposed model outperforms traditional methods in both prediction accuracy and uncertainty quantification capability, providing an effective tool for the life assessment and reliability design of blade materials under extreme environments.

Key words: SiC/SiC Ceramic Matrix Composites, Creep, Stochastic Process, Metabolic GM (1,1), Grey Cloud Model