Health monitoring of FRP using acoustic emission and artificial neural networks

Authors:
R. de Oliveira;A. T. Marques
Affiliations:
INEGI, Rua do Barroco, 174, 4465-591 Leça do Balio, Portugal;Departamento de Engenharia Mecínica e Gestão Industrial (DEMEGI), Faculdade de Engenharia da Universidade do Porto (FEUP), Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal
Venue:
Computers and Structures
Year:
2008

Citing 3
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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.