Damage Mechanisms Identification in FRP Using Acoustic Emission and Artificial Neural Networks
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.
Paula Maria Vilarinho
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