Damage Mechanisms Identification in FRP Using Acoustic Emission and Artificial Neural Networks

Abstract:

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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 and 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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Price:

$38.00

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