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Proceedings Paper

Curvature methods of damage detection using digital image correlation
Author(s): Mark N. Helfrick; Christopher Niezrecki; Peter Avitabile
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Paper Abstract

Analytical models have shown that local damage in a structure can be detected by studying changes in the curvature of the structure's displaced shape while under an applied load. In order for damage to be detected, located, and quantified using curvature methods, a spatially dense set of measurement points is required on the structure of interest and the change in curvature must be measurable. Experimental testing done to validate the theory is often plagued by sparse data sets and experimental noise. Furthermore, the type of load, the location and severity of the damage, and the mechanical properties (material and geometry) of the structure have a significant effect on how much the curvature will change. Within this paper, three-dimensional (3D) Digital Image Correlation (DIC) as one possible method for detecting damage through curvature methods is investigated. 3D DIC is a non-contacting full-field measurement technique which uses a stereo pair of digital cameras to capture surface shape. This approach allows for an extremely dense data set across the entire visible surface of an object. A test is performed to validate the approach on an aluminum cantilever beam. A dynamic load is applied to the beam which allows for measurements to be made of the beam's response at each of its first three resonant frequencies, corresponding to the first three bending modes of the structure. DIC measurements are used with damage detection algorithms to predict damage location with varying levels of damage inflicted in the form of a crack with a prescribed depth. The testing demonstrated that this technique will likely only work with structures where a large displaced shape is easily achieved and in cases where the damage is relatively severe. Practical applications and limitations of the technique are discussed.

Paper Details

Date Published: 8 April 2009
PDF: 12 pages
Proc. SPIE 7295, Health Monitoring of Structural and Biological Systems 2009, 72950D (8 April 2009); doi: 10.1117/12.815511
Show Author Affiliations
Mark N. Helfrick, Univ. of Massachusetts Lowell (United States)
Christopher Niezrecki, Univ. of Massachusetts Lowell (United States)
Peter Avitabile, Univ. of Massachusetts Lowell (United States)

Published in SPIE Proceedings Vol. 7295:
Health Monitoring of Structural and Biological Systems 2009
Tribikram Kundu, Editor(s)

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