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

N. Fallahi, G. Nardoni, H. Heidary, R. Palazzetti, X.T. Yan, A. Zucchelli

Research output: Contribution to journalArticle

6 Citations (Scopus)
91 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)415-421
Number of pages7
JournalFME Transactions
Volume44
Issue number4
DOIs
Publication statusAccepted/In press - 21 Jun 2016

Keywords

  • acoustic emission
  • artificial neural network
  • K-means
  • CFRP
  • electrospinning

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