Paper Title:
Damage Mechanisms Identification in FRP Using Acoustic Emission and Artificial Neural Networks
  Abstract

In this study is proposed a procedure for damage discrimination based on acoustic emission signals clustering using artificial neural networks. An unsupervised methodology based on the self-organizing maps of Kohonen is developed considering the lack of a priori knowledge of the different signal classes. The methodology is described and applied to a cross-ply glassfibre/ polyester laminate submitted to a tensile test. In this case, six different AE waveforms were identified. The damage sequence could so be identified from the modal nature of those waves.

  Info
Periodical
Materials Science Forum (Volumes 514-516)
Edited by
Paula Maria Vilarinho
Pages
789-793
DOI
10.4028/www.scientific.net/MSF.514-516.789
Citation
R. de Oliveira, A. T. Marques, "Damage Mechanisms Identification in FRP Using Acoustic Emission and Artificial Neural Networks ", Materials Science Forum, Vols. 514-516, pp. 789-793, 2006
Online since
May 2006
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