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Failure detection and monitoring in polymer matrix composites subjected to static and dynamic loads using carbon nanotube networks

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Failure detection and monitoring in polymer matrix composites subjected to static and dynamic loads using carbon nanotube networks

Nofar, Mohammadreza (2008) Failure detection and monitoring in polymer matrix composites subjected to static and dynamic loads using carbon nanotube networks. Masters thesis, Concordia University.

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Abstract

In this work, multiwall carbon nanotubes (MWCNTs) have been used as a network of sensors to predict the failure region and to monitor the degradation of mechanical properties in laminated composites subjected to tensile and cyclic fatigue loadings. This is achieved by measuring the electrical resistance change in the semi-conductive MWCNT-fiber glass-epoxy polymer matrix composites. By partitioning the tensile and fatigue samples with electrically conductive probes, it has been shown that with both increasing tensile load and number of cycles, different resistance changes are detected in different regions and failure happens in the part in which higher resistance change was detected. In cyclic loading, in which the maximum load is higher than the elastic limit of the laminate, a sharp increase in resistance occurs within the first several cycles. There is also a change in resistance during long term cyclic loading. In cyclic loading, when compared to strain gauge readings, resistance change measurements show more sensitivity in identifying the crack initiation site, which gives this technique a good potential for monitoring strength degradation during fatigue. Keywords: composites; Carbon nanotubes; Electrical properties; Sensors.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical and Industrial Engineering
Item Type:Thesis (Masters)
Authors:Nofar, Mohammadreza
Pagination:xi, 84 leaves : ill. ; 29 cm.
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Mechanical and Industrial Engineering
Date:2008
Thesis Supervisor(s):Hoa, S. V and Pugh, M
ID Code:976270
Deposited By: Concordia University Library
Deposited On:22 Jan 2013 16:22
Last Modified:18 Jan 2018 17:42
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