Supervised and non-supervised AE data classification of nanomodified CFRP during DCB tests
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: https://orcid.org/0000-0003-3813-4090, Yan, X.T. ORCID: https://orcid.org/0000-0002-3798-7414 and Zucchelli, A.;-
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Item type: Article ID code: 57403 Dates: DateEvent21 June 2016Published21 June 2016Accepted6 June 2016SubmittedSubjects: Technology > Engineering (General). Civil engineering (General) Department: Faculty of Engineering > Design, Manufacture and Engineering Management Depositing user: Pure Administrator Date deposited: 13 Aug 2016 00:05 Last modified: 14 Oct 2024 00:28 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/57403