Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
511091 | Computers & Structures | 2008 | 7 Pages |
Abstract
In this study, a procedure is proposed for damage identification and discrimination for composite materials based on acoustic emission signals clustering using artificial neural networks. An unsupervised methodology based on the self-organizing map of Kohonen is developed. The methodology is described and applied to a cross-ply glass-fibre/polyester laminate submitted to a tensile test. Six different AE waveforms were identified. Hence, the damage sequence has been identified from the modal nature of the AE waves.
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Physical Sciences and Engineering
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Computer Science Applications
Authors
R. de Oliveira, A.T. Marques,