E xs civilization v image3/5/2023 ![]() The human inspection method requires large testing times since it needs to interrupt the fatigue test to manually measure crack lengths. ![]() Existing approaches including the human inspection method, the electrical method, the compliance method, and acoustic emission technology all have limitations for application during a fatigue test. In recent decades, extensive research on monitoring crack length propagation has been carried out. Crack length is one of the most relevant parameters that needs to be recorded during laboratorial tests. Fatigue crack propagation testing is an essential method of studying metallic or structural fatigue life prediction in fracture mechanics. Furthermore, crack length could be measured with submillimeter accuracy.įatigue cracks caused by repetitive loads, which are of great concern for structural safety, always exist in old structures including airplane and highway bridges. The results demonstrated that the proposed approach could robustly identify a fatigue crack surrounded by crack-like noises and locate the crack tip accurately. The effectiveness and precision of the proposed approach were validated through conducting fatigue experiments. ![]() Then, a crack tip-detection algorithm was established to accurately locate the crack tip and was used to calculate the length of the crack. Convolutional neural networks were first applied to robustly detect the location of cracks with the interference of scratch and edges. In this paper, a new framework based on convolutional neural networks (CNN) and digital image processing is proposed to monitor crack propagation length. Traditional vision-based methods are insufficient in distinguishing cracks from noises and detecting crack tips. Human inspection is the most widely used approach for fatigue failure detection, which is time consuming and subjective. Fatigue failure is a significant problem in the structural safety of engineering structures.
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