Statistical learning prediction of fatigue crack growth via path slicing and re-weighting
作者机构:Applied Mechanics LaboratoryDepartment of Engineering MechanicsTsinghua UniversityBeijing100084China
出 版 物:《Theoretical & Applied Mechanics Letters》 (力学快报(英文版))
年 卷 期:2023年第13卷第6期
页 面:415-423页
核心收录:
学科分类:08[工学] 0801[工学-力学(可授工学、理学学位)] 080102[工学-固体力学]
基 金:the National Natural Science Foundation of China(Grant Nos.52090032 and 11825203)
主 题:Fatigue crack growth Structural health monitoring Statistical noises Rare events Digital libraries
摘 要:Predicting potential risks associated with the fatigue of key structural components is crucial in engineering ***,fatigue often involves entangled complexities of material microstructures and service conditions,making diagnosis and prognosis of fatigue damage *** report a statistical learning framework to predict the growth of fatigue cracks and the life-to-failure of the components under loading conditions with *** libraries of fatigue crack patterns and the remaining life are constructed by high-fidelity physical *** reduction and neural network architectures are then used to learn the history dependence and nonlinearity of fatigue crack ***-slicing and re-weighting techniques are introduced to handle the statistical noises and rare *** predicted fatigue crack patterns are self-updated and self-corrected by the evolving crack *** end-to-end approach is validated by representative examples with fatigue cracks in plates,which showcase the digital-twin scenario in real-time structural health monitoring and fatigue life prediction for maintenance management decision-making.