Health monitoring of FRP using acoustic emission and artificial neural networks

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.