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

FME Transactions

Fallahi N.¹  Nardoni G.¹  Heidary Hossein²  Zucchelli A.³  Palazzetti R.⁴  Yan X.T.⁵ 

[1] I & T Nardoni Institute, Brescia, Italy;Tafresh University of Technology, Tafresh, Iran;University of Bologna, Bologna, Italy;University of Strathclyde, DMEM department, Glasgow, UK;University of Strathclyde, Glasgow, UK;

关键词: acoustic emissions;    carbon-epoxy composites;    electrospinning;    k-means;    artificial neural network;   

DOI  :  

来源: DOAJ

【 摘 要 】

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, 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.

【 授权许可】

Unknown