Supervised and non-supervised AE data classification of nanomodified CFRP during DCB tests

N. Fallahi, G. Nardoni, Hossein Heidary, R. Palazzetti, X.T. Yan, A. Zucchelli
2016 FME Transaction  
The aim of the paper is to use acoustic emissions to study the effect of electrospun nylon 6,6 Nanofibrous mat on carbon-epoxy composites during Double Cantilever beam (DCB) tests. In order to recognize the effect of the nanofibres and to detect different damage mechanisms, kmeans clustering of acoustic emission signals applied to rise time, count, energy, duration and amplitude of the events is used. Supervised neural network (NN) is then applied to verify clustered signals. Results showed
more » ... Results showed that clustered acoustic emission signals are a reliable tool to detect different damage mechanisms; neural network showed the method has a 99% of accuracy.
doi:10.5937/fmet1604415f fatcat:giw5i7l5hrckbn674ayktmfkny