Predicting Fatigue Crack Growth via Path Slicing and Re-Weighting
Yingjie Zhao Yong Liu Zhiping Xu
Predicting potential risks associated with the fatigue of key structural
components is crucial in engineering design. However, fatigue often involves
entangled complexities of material microstructures and service conditions,
making diagnosis and prognosis of fatigue damage challenging. We report a
statistical learning framework to predict the growth of fatigue cracks and the
life-to-failure of the components under loading conditions with uncertainties.
Digital libraries of fatigue crack patterns and the remaining life are
constructed by high-fidelity physical simulations. Dimensionality reduction and
neural network architectures are then used to learn the history dependence and
nonlinearity of fatigue crack growth. Path-slicing and re-weighting techniques
are introduced to handle the statistical noises and rare events. The predicted
fatigue crack patterns are self-updated and self-corrected by the evolving
crack patterns. The 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.