Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Pattern recognition of fiber-reinforced plastic failure mechanism using computational intelligence techniques

Tools
- Tools
+ Tools

Li, XuQin, Ramirez, Carlos, Hines, Evor, Leeson, Mark S., Purnell, Phil and Pharaoh, Mark W. (2008) Pattern recognition of fiber-reinforced plastic failure mechanism using computational intelligence techniques. In: International Joint Conference on Neural Networks, Hong Kong, China, Jun 01-08, 2008. Published in: Proceedings of the 2008 IEEE International Joint Conference on Neural Networks, Vol.1-8 pp. 2340-2345. ISBN 978-1-4244-1820-6. doi:10.1109/IJCNN.2008.4634122 ISSN 1098-7576.

Research output not available from this repository.

Request-a-Copy directly from author or use local Library Get it For Me service.

Official URL: http://dx.doi.org/10.1109/IJCNN.2008.4634122

Request Changes to record.

Abstract

Acoustic Emission (AE) can be used to discriminate the different types of damage occurring in composite materials, because any AE signal contains useful information about the damage mechanisms. A major issue in the use of the AE technique is how to discriminate the AE signatures which are due to the different damage mechanisms Conventional studies have focused on the analysis of different parameters of such signals, say the frequency. But in previous publications where the frequency is employed to differentiate between events, only one frequency is considered and this frequency was not enough to thoroughly describe the behavior of the composite material. So we introduced the second frequency. A Fast Fourier Transform (FFT) is then applied to the signals resulting from the two frequencies to discriminate different failure mechanisms. This was achieved by using self-organizing map and Fuzzy C-means to cluster the AE data. The result shows that the two approaches have been very successful.

Item Type: Conference Item (Paper)
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science, Engineering and Medicine > Engineering > Engineering
Series Name: IEEE International Joint Conference on Neural Networks (IJCNN)
Journal or Publication Title: Proceedings of the 2008 IEEE International Joint Conference on Neural Networks
Publisher: Institute of Electrical and Electronic Engineers
ISBN: 978-1-4244-1820-6
ISSN: 1098-7576
Official Date: 2008
Dates:
DateEvent
2008Published
Volume: Vol.1-8
Number of Pages: 6
Page Range: pp. 2340-2345
DOI: 10.1109/IJCNN.2008.4634122
Status: Not Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Funder: Warwick Postgraduate Research Fellowship (WPRF), Overseas Research Students Award Scheme (ORSAS)
Conference Paper Type: Paper
Title of Event: International Joint Conference on Neural Networks
Type of Event: Conference
Location of Event: Hong Kong, China
Date(s) of Event: Jun 01-08, 2008

Data sourced from Thomson Reuters' Web of Knowledge

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item
twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us