2/24/2023 0 Comments Crack ni vision builderPiezoelectric wafer active sensors (PWAS) can effectively sense acoustic waves due to fatigue-crack growth. The strain energy released when a fatigue crack advances has the effect of exciting acoustic waves, which travel through the structures and are picked up by the sensors. Furthermore, crack length could be measured with submillimeter accuracy.Īcoustic waves are widely used in structural health monitoring (SHM) for detecting fatigue cracking. 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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