Analysis of Acoustic Emission Output from Propagating Fatigue Crack

Daniel, I. M.; Sifniotopoulos, C. G.; Luo, Jianqiang

doi:10.1007/978-1-4615-5339-7_75

Cited by 2 publications

(1 citation statement)

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“…They reported that crack growth increased smoothly with fatigue cycles and that AE activity increased near the middle of the fatigue test. Daniel et al 9 investigated the application of high-pass frequency filtering, threshold filtering, and spatial filtering techniques for processing AE signals from fatigue tests. Crack extensions were monitored with two different crack propagation gages.…”

Section: Introductionmentioning

confidence: 99%

Acoustic emission detection and prediction of fatigue crack propagation in composite patch repairs using neural network

Okafor

Singh

et al. 2016

Journal of Thermoplastic Composite Materials

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This article presents the results of acoustic emission (AE) monitoring of crack propagation in 2024-T3 clad aluminum panels repaired with adhesively bonded octagonal and elliptical boron/epoxy composite patches using FM-73 adhesive under tension-tension fatigue loading. Two crack propagation gages and four broadband AE sensors were used to monitor crack initiation and propagation, respectively. The acquired AE signals were processed in time and frequency domain to identify sensor features correlated with fatigue cycle and crack propagation, which were used to train neural networks for predicting crack length. The results show that AE events are correlated with crack propagation, and crack propagation signals can be differentiated from signals due to matrix cracking, fiber breakage, and shear of the composite patch. Three backpropagation cascade feed-forward networks were trained to predict crack length using number of fatigue cycles, number of AE events, and number of fatigue cycles and number of AE events together as inputs, respectively. It was found that network with fatigue cycles as input gave good results, while the network with just AE events as input gave greater error. However, the network using both fatigue cycles and number of AE events as inputs to predict crack length gave much better results.

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Section: Introductionmentioning

confidence: 99%