Structural health monitoring for woven fabric CFRP laminates


Structural health monitoring is directly linked to structural performance, hence it is one of the main parameters in the safety of operation. This paper presents the development of an innovative structural health monitoring system for woven fabric carbon fibre reinforced polymer (CFRP) laminates fabricated using both vacuum assisted resin transfer moulding and pre-preg technique. The sensing system combines the ability to monitor strain due to applied loads, as well as to detect, and assess damage due to low velocity impact events. Bending loads were applied on a beam-type specimen and changes in electrical resistance, due to piezoresistivity of carbon fibres, were monitored. The change in electrical resistance was a function of applied load and reversible up to 0.13 % strain. Two thicknesses of composite panel, 2.09 (vacuum assisted resin transfer moulding) and 1.63 mm (pre-preg) were made, and were subjected to a range of low velocity impact energies. The resultant damage areas, as measured using ultrasonic C-scanning, were plotted against changes in electrical resistance to provide a correlation plot of damage area against impact energy. An inverse analysis, using this correlation plot, was performed to predict the damage area from a known impact event. 85 % accuracy in the predicted damage area was achieved in comparison with subsequent C-scan data on the unknown damage.

Divisions: College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design
College of Engineering & Physical Sciences
Additional Information: © 2019, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Publication ISSN: 1879-1069
Last Modified: 29 Nov 2023 12:22
Date Deposited: 02 Jul 2019 14:19
Full Text Link: 10.1016/j.compo ... esb.2019.107048
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.sci ... 0005?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2019-10-01
Published Online Date: 2019-06-13
Accepted Date: 2019-06-12
Authors: Al-Saadi, Ahmed
Meredith, James
Swait, Tim
Curiel-Sosa, Jose
Jia, Yu
Hayes, Simon

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