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Proceedings Paper

Full-scale ferromagnetic active tagging testing of C-channel composite elements
Author(s): Zao Chen; Harley H. Cudney; Victor Giurgiutiu; Craig A. Rogers; Robert Quattrone; Justin Berman
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Paper Abstract

This paper presents the work we have done on a health monitoring technique called active non-destructive evaluation (ANDE) for detecting delaminations in full-scale C-channel structural elements of glass-fiber reinforced polymer (GFRP) composites using active ferromagnetic tagging. Conventional non-destructive evaluation methods are not very effective in monitoring the material conditions of GFRP composite and adhesive joints. A technology that has been proposed to enhance inspection of such non-conductive and non-magnetic GFRP composites is the particle tagging technique. This technique, previously demonstrated on small scale laboratory samples is being developed for full-scale C-channel composite elements. This technique relies on comparing changes in local-area mechanical properties of the structure to identify delaminations. Unlike conventional passive tagging NDE inspection, our technique uses an electromagnetic exciter to interrogate tagged composites over a broad frequency range. As the vibration excitation approaches the resonant frequency of the area of the disbondment, the response amplitude obtained for a given force input increases. Therefore, at frequencies around the transverse resonance of the layer above a defect, the response for a given force will be greater than the response in flawless regions of the structure. Thus, the ANDE test may be based on response measurements alone. The technique is most sensitive if excitation is applied at the plate resonant frequency. Since this frequency is dependent on defect size and depth, a broad band of frequencies must be covered. A laser Doppler vibrometer is a high-sensitivity, high-speed and non-contacting instrument used for detecting surface vibrations. The frequency response curves are obtained using a fast Fourier transform signal processor. An algorithm based on training a neural network to detect significant differences between healthy and damaged structures is then applied to recognize delaminations in full scale structural elements. This method provides the fundamental technology needed for developing a commercial system to monitor the integrity of composite structures, both during manufacturing and during their lifetime as structural elements.

Paper Details

Date Published: 23 May 1997
PDF: 12 pages
Proc. SPIE 3043, Smart Structures and Materials 1997: Smart Systems for Bridges, Structures, and Highways, (23 May 1997); doi: 10.1117/12.274640
Show Author Affiliations
Zao Chen, Virginia Polytechnic Institute and State Univ. (United States)
Harley H. Cudney, Virginia Polytechnic Institute and State Univ. (United States)
Victor Giurgiutiu, Univ. of South Carolina/Columbia (United States)
Craig A. Rogers, Univ. of South Carolina/Columbia (United States)
Robert Quattrone, U.S. Army Corps of Engineers (United States)
Justin Berman, U.S. Army Corps of Engineers (United States)

Published in SPIE Proceedings Vol. 3043:
Smart Structures and Materials 1997: Smart Systems for Bridges, Structures, and Highways
Norris Stubbs, Editor(s)

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