Journal
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 25, Issue 4, Pages 1393-1407Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2010.11.014
Keywords
Failure; Composites; Acoustic emission; Neural network; Frequency
Categories
Funding
- Engineering and Physical Sciences Research Council
- Ministry of Defence [EP/E0Z3169/1]
- Engineering and Physical Sciences Research Council [EP/E023169/1] Funding Source: researchfish
- EPSRC [EP/E023169/1] Funding Source: UKRI
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This paper investigates failure in Carbon Fibre Reinforced Plastics CFRP using Acoustic Emission (AE). Signals have been collected and post-processed for various test configurations: tension, Compact Tension (CT), Compact Compression (CC), Double Cantilever Beam (DCB) and four-point bend End Notched Flexure (4-ENF). The signals are analysed with three different pattern recognition algorithms: k-means, Self Organising Map (SOM) combined with k-means and Competitive Neural Network (CNN). The SOM combined with k-means appears as the most effective of the three algorithms. The results from the clustering analysis follow patterns found in the peak frequencies distribution. A detailed study of the frequency content of each test is then performed and the classification of several failure modes is achieved. (C) 2010 Elsevier Ltd. All rights reserved.
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