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

Tools

Fallahi, N. and Nardoni, G. and Heidary, H. and Palazzetti, R. and Yan, X.T. and Zucchelli, A. (2016) Supervised and non-supervised AE data classification of nanomodified CFRP during DCB tests. FME Transactions, 44 (4). pp. 415-421. ISSN 2406-128X (In Press) (https://doi.org/10.5937/fmet1604415F)

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Abstract

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, k-means 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 that clustered acoustic emission signals are a reliable tool to detect different damage mechanisms; neural network showed the method has a 99% of accuracy.

ORCID iDs

Fallahi, N., Nardoni, G., Heidary, H., Palazzetti, R. ORCID logoORCID: https://orcid.org/0000-0003-3813-4090, Yan, X.T. ORCID logoORCID: https://orcid.org/0000-0002-3798-7414 and Zucchelli, A.;
CORE (COnnecting REpositories)