Predicting Fatigue Crack Growth via Path Slicing and Re-Weighting
This work addresses fatigue prediction for structural health monitoring in engineering, though it appears incremental as it builds on existing statistical and neural network methods with specific techniques like path-slicing.
The authors tackled the challenge of predicting fatigue crack growth and life-to-failure in structural components under uncertain loading conditions by developing a statistical learning framework that uses digital libraries from simulations, dimensionality reduction, and neural networks, achieving validation through representative examples in plates for real-time monitoring and decision-making.
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.