@inproceedings{84084ce735e647f39394b834b9e1ffc4,
title = "Creep-Fatigue Life Prediction of FGH96 Based on Advanced Physical-Information Neural Network",
abstract = "A creep-fatigue life prediction model based on a Physics-Informed Neural Network (PINN) is proposed to accurately evaluate the service life of FGH96 turbine disc materials under complex loading conditions. This method combines the powerful nonlinear fitting ability of neural networks with physical boundary constraints, such as frequency correction models, to improve the predictive performance of the model while ensuring that it conforms to the actual physical mechanisms. The experimental results show that the predicted lives by this method all fall within 1.8 times the scatter band of the experimental life, and the life prediction of FGH96 alloy by this method is more consistent with the experimental results compared with the traditional creep-based fatigue life model and the purely data-driven model.",
keywords = "Creep-fatigue, FGH96, lifetime prediction, physical information neural network",
author = "Tianbao Shen and Dianyin Hu and Yan Zhao and Gaoxiang Chen and Ruoqi Chen and Yupeng Liu",
note = "Publisher Copyright: {\textcopyright} 2026 The Authors.; 16th International Conference of Mechanical and Aerospace Engineering, ICMAE 2025 ; Conference date: 15-07-2025 Through 18-07-2025",
year = "2026",
month = mar,
day = "3",
doi = "10.3233/ATDE260021",
language = "英语",
series = "Advances in Transdisciplinary Engineering",
publisher = "IOS Press BV",
pages = "63--73",
editor = "Xuelin Lei",
booktitle = "Moving Integrated Product Development to Service Clouds in the Global Economy - Proceedings of the 21st ISPE Inc. International Conference on Concurrent Engineering, CE 2014",
address = "荷兰",
}