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Title: Acoustic Emission Detection and Prediction of Fatigue Crack Propagation in Composite Patch Repairs Using Neural Networks

Abstract

An aircraft is subjected to severe structural and aerodynamic loads during its service life. These loads can cause damage or weakening of the structure especially for aging military and civilian aircraft, thereby affecting its load carrying capabilities. Hence composite patch repairs are increasingly used to repair damaged aircraft metallic structures to restore its structural efficiency. This paper presents the results of Acoustic Emission (AE) monitoring of crack propagation in 2024-T3 Clad aluminum panels repaired with adhesively bonded octagonal, single sided boron/epoxy composite patch under tension-tension fatigue loading. Crack propagation gages were used to monitor crack initiation. The identified AE sensor features were used to train neural networks for predicting crack length. The results show that AE events are correlated with crack propagation. AE system was able to detect crack propagation even at high noise condition of 10 Hz loading; that crack propagation signals can be differentiated from matrix cracking signals that take place due to fiber breakage in the composite patch. Three back-propagation cascade feed forward networks were trained to predict crack length based on the number of fatigue cycles, AE event number, and both the Fatigue Cycles and AE events, as inputs respectively. Network using both fatigue cycles andmore » AE event number as inputs to predict crack length gave the best results, followed by Network with fatigue cycles as input, while network with just AE events as input had a greater error.« less

Authors:
; ;  [1]
+ Show Author Affiliations
  1. Structural Health Monitoring and NDE Laboratory, Department of Mechanical and Aerospace Engineering, University of Missouri-Rolla, 1870 Miner Circle Rolla MO 65409-0050 (United States)
Publication Date:
OSTI Identifier:
21054948
Resource Type:
Journal Article
Resource Relation:
Journal Name: AIP Conference Proceedings; Journal Volume: 894; Journal Issue: 1; Conference: Conference on review of progress in quantitative nondestructive evaluation, Portland, OR (United States), 30 Jul - 4 Aug 2006; Other Information: DOI: 10.1063/1.2718147; (c) 2007 American Institute of Physics; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; ACOUSTIC EMISSION TESTING; AGING; AIRCRAFT; ALUMINIUM; ALUMINIUM ALLOYS; BORON; COMPOSITE MATERIALS; CRACK PROPAGATION; CRACKS; DETECTION; FATIGUE; FIBERS; LOADING; MAINTENANCE; NEURAL NETWORKS; POLYMERS; SENSORS

Citation Formats

Okafor, A. Chukwujekwu, Singh, Navdeep, and Singh, Navrag. Acoustic Emission Detection and Prediction of Fatigue Crack Propagation in Composite Patch Repairs Using Neural Networks. United States: N. p., 2007. Web. doi:10.1063/1.2718147.
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Okafor, A. Chukwujekwu, Singh, Navdeep, & Singh, Navrag. Acoustic Emission Detection and Prediction of Fatigue Crack Propagation in Composite Patch Repairs Using Neural Networks. United States. doi:10.1063/1.2718147.
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Okafor, A. Chukwujekwu, Singh, Navdeep, and Singh, Navrag. Wed . "Acoustic Emission Detection and Prediction of Fatigue Crack Propagation in Composite Patch Repairs Using Neural Networks". United States. doi:10.1063/1.2718147.
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@article{osti_21054948,
title = {Acoustic Emission Detection and Prediction of Fatigue Crack Propagation in Composite Patch Repairs Using Neural Networks},
author = {Okafor, A. Chukwujekwu and Singh, Navdeep and Singh, Navrag},
abstractNote = {An aircraft is subjected to severe structural and aerodynamic loads during its service life. These loads can cause damage or weakening of the structure especially for aging military and civilian aircraft, thereby affecting its load carrying capabilities. Hence composite patch repairs are increasingly used to repair damaged aircraft metallic structures to restore its structural efficiency. This paper presents the results of Acoustic Emission (AE) monitoring of crack propagation in 2024-T3 Clad aluminum panels repaired with adhesively bonded octagonal, single sided boron/epoxy composite patch under tension-tension fatigue loading. Crack propagation gages were used to monitor crack initiation. The identified AE sensor features were used to train neural networks for predicting crack length. The results show that AE events are correlated with crack propagation. AE system was able to detect crack propagation even at high noise condition of 10 Hz loading; that crack propagation signals can be differentiated from matrix cracking signals that take place due to fiber breakage in the composite patch. Three back-propagation cascade feed forward networks were trained to predict crack length based on the number of fatigue cycles, AE event number, and both the Fatigue Cycles and AE events, as inputs respectively. Network using both fatigue cycles and AE event number as inputs to predict crack length gave the best results, followed by Network with fatigue cycles as input, while network with just AE events as input had a greater error.},
doi = {10.1063/1.2718147},
journal = {AIP Conference Proceedings},
number = 1,
volume = 894,
place = {United States},
year = {Wed Mar 21 00:00:00 EDT 2007},
month = {Wed Mar 21 00:00:00 EDT 2007}
}
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Journal Article:
DOI:  10.1063/1.2718147
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    A burst pressure prediction model was generated from the acoustic emission amplitude distribution data taken during hydroproof for three sets of ASTM standard 145 mm (5.75 in.) diameter filament wound graphite/epoxy bottles. The three sets of bottles featured the same design parameters and were wound from the same graphite fiber, the only difference being in the epoxies used. Therefore, the three resin types were categorized using dummy variables, which allowed the prediction of burst pressures in all three sets of bottles using a single back-propagation neural network. Three bottles from each set were used to train the network. The resinmore » category and the acoustic emission amplitude distribution data taken up to 25 percent of the expect burst pressure were used as network inputs. The actual burst pressures were supplied as target values for the supervised training phase. Architecturally, the network consisted of a 48 neuron input layer (a categorical variable defining the resin type, plus 47 integer variables for the acoustic emission amplitude distribution frequencies), a 15 neural hidden layer for mapping, and a single output neuron for burst pressure prediction. The network, trained on three bottles from each resin type, was able to predict burst pressures in the remaining bottles with a worst case error of {minus}3.89 percent, well within the desired goal of {+-}5 percent.« less

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