Optimized FBG sensor network for efficient detection of a delamination in FRP structures

Kahandawa, G. C. and Epaarachchi, J. A. and Wang, H. and Lau, K. T. (2012) Optimized FBG sensor network for efficient detection of a delamination in FRP structures. In: 8th Asian-Australasian Conference on Composite Materials (ACCM 2012): Composites: Enabling Tomorrow's Industry Today, 6-8 Nov 2012, Kuala Lumpur, Malaysia.

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Abstract

Delamination is a potential cause of failure of composite components. Due to the hidden nature of propagation, the detection of delaminations in composites is a time consuming and extremely difficult task. A few decades of research have shown the effectiveness of the embedded fibre Bragg grating (FBG) sensors to detect such damage in fibre reinforced polymeric (FRP) structures. However, a number of sensors are required to detect delaminations within a particular region of a composite structure due the limited receptive range of an FBG sensor. The complexity and the cost of manufacturing increases with the number of sensors attached and therefore, estimation of the optimum number of sensors for efficient identification of damage is an equally important factor to investigate.
This paper details a study on optimization of the number of sensors used to monitor damage in a critical region of an FRP structure. A detailed finite element analysis (FEA) was used for the investigation. A delamination and several FBG sensors were simulated in FEA. The strain values at simulated FBG sensors were used as an input for the development of an optimization algorithm, using artificial neural network (ANN). The number of FBG sensors was decreased until the prediction of the algorithm was reached within a 0.1% error level. The optimal number of FBGs was taken at 0.1% error level with a minimum number of epoch. Furthermore, the effect of obsolete sensors of an optimized sensor network on prediction of the delamination, was also investigated.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Copyright© (2012) by Asian-Australasian Association for Composite Materials (AACM). Permanent restricted access to published version in accordance with the copyright policy of the publisher.
Faculty / Department / School: Historic - Faculty of Engineering and Surveying - Department of Mechanical and Mechatronic Engineering
Date Deposited: 16 May 2013 01:05
Last Modified: 27 Oct 2014 04:54
Uncontrolled Keywords: FBG sensors; composite materials; finite element analysis; structural health monitoring; artificial neural networks
Fields of Research : 09 Engineering > 0901 Aerospace Engineering > 090103 Aerospace Structures
02 Physical Sciences > 0205 Optical Physics > 020504 Photonics, Optoelectronics and Optical Communications
09 Engineering > 0912 Materials Engineering > 091202 Composite and Hybrid Materials
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
Socio-Economic Objective: B Economic Development > 87 Construction > 8703 Construction Materials Performance and Processes > 870399 Construction Materials Performance and Processes not elsewhere classified
URI: http://eprints.usq.edu.au/id/eprint/22681

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