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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]
  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.
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.
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.
@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}
}
  • 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
  • Acoustic emission signal analysis has been used to measure the effect impact damage has on the burst pressure of 146 mm (5.75 in.) diameter graphite/epoxy and the organic polymer, Kevlar/epoxy filament wound pressure vessels. Burst pressure prediction models were developed by correlating the differential acoustic emission amplitude distribution collected during low level hydroproof tests to known burst pressures using backpropagation artificial neural networks. Impact damage conditions ranging from barely visible to obvious fiber breakage, matrix cracking, and delamination were included in this work. A simulated (inert) propellant was also cast into a series of the vessels from each material class,more » before impact loading, to provide boundary conditions during impact that would simulate those found on solid rocket motors. The results of this research effort demonstrate that a quantitative assessment of the effects that impact damage has on burst pressure can be made for both organic polymer/epoxy and graphite/epoxy pressure vessels. Here, an artificial neural network analysis of the acoustic emission parametric data recorded during low pressure hydroproof testing is used to relate burst pressure to the vessel`s acoustic signature. Burst pressure predictions within 6.0% of the actual failure pressure are demonstrated for a series of vessels.« less
  • The acoustic emission (AE) behavior during fatigue propagation in aluminum 6061 and aluminum 6061 matrix composites containing 5, 10, and 20 wt pct SiC particle reinforcement was investigated under tension-tension fatigue loading. The purpose of this investigation was to monitor fatigue crack propagation by the AE technique and to identify the source(s) of AE. Most of the AEs detected were observed at the top of the load cycles. The cumulative number of AE events was found to correspond closely to the fatigue crack growth and to increase with increasing SiC content. Fractographic studies revealed an increasing number of fractured particlesmore » and to a lesser extent decohered particles on the fatigue fracture surface as the crack propagation rate (e.g., {Delta}K) or the SiC content was increased.« less
  • An artificial neural network method is developed to represent the fatigue crack growth and cycle relationships under different spectrum loadings. The method utilizes load cycle spectrum using available flight data and experimental data for crack growth vs cycles as input. The trained network is able to predict the relationship between the crack growth and loading cycles. The neural network is able to generalize the crack growth-cycle behavior for different variations in the loading spectrums. The result predicted by the neural network model seems reasonable and the model is capable of representing crack growth behavior for arbitrary loadings. 7 refs.