Statistical learning prediction of fatigue crack growth via path slicing and re-weighting

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.

Automated summary

This paper presents a statistical learning framework for predicting the growth of fatigue cracks and the life-to-failure of components. Digital libraries of fatigue crack patterns and remaining life are constructed using physical simulations. Dimensionality reduction and neural network architectures are utilized to capture the history dependence and nonlinearity of fatigue crack growth. Path-slicing and re-weighting techniques are introduced to handle statistical noises and rare events. The end-to-end approach is validated with representative examples of fatigue cracks in plates.