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Characterization and Localization of Sub-Surface Structural Features Using Non-Contact Tomography

[+] Author Affiliations
Sumit Gupta, Kenneth J. Loh

University of California, San Diego, La Jolla, CA

Paper No. SMASIS2016-9030, pp. V001T05A007; 8 pages
doi:10.1115/SMASIS2016-9030
From:
  • ASME 2016 Conference on Smart Materials, Adaptive Structures and Intelligent Systems
  • Volume 1: Multifunctional Materials; Mechanics and Behavior of Active Materials; Integrated System Design and Implementation; Structural Health Monitoring
  • Stowe, Vermont, USA, September 28–30, 2016
  • Conference Sponsors: Aerospace Division
  • ISBN: 978-0-7918-5048-0
  • Copyright © 2016 by ASME

abstract

The main objective of this work is to develop a non-contact, non-invasive, structural health monitoring technique for surface and sub-surface damage detection in structures such as composite helicopter rotor blades. In many cases, composite structures are prone to damage in the form of cracks, delamination, and manufacturing defects, which can propagate beneath structural surfaces and cause severe component or catastrophic structural failure. The damage detection technique in this study works on the principle of electrical capacitance tomography. Different patterns of electrical field are propagated in a pre-defined sensing area. Using measurements of electrical response along boundaries of the sensing area, the permittivity distribution within that space can be reconstructed. First, a series of numerical simulations was performed by altering the electrical permittivity at different locations to simulate damage. The shapes and locations of permittivity changes were captured by the proposed technique. Second, to demonstrate its validity, an experimental test setup was built with a set of boundary electrodes. The system was connected to a function generator that supplied an electrical signal and induced electrical fields between electrodes. Capacitance between pairs of electrodes were then measured, which were used as inputs for solving the inverse permittivity reconstruction problem. Various test cases with different objects placed in the sensing area were conducted for validating this technique. The preliminary results show that the system was able to reconstruct spatial permittivity distributions and detect the presence, shapes, and locations of objects, thereby suggesting potential for damage detection.

Copyright © 2016 by ASME

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