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Neural Network Based Processing of Thermal NDE Data for Corrosion Detection

  • D. R. Prabhu
  • W. P. Winfree

Abstract

Subsurface corrosion in aircraft structure is a major concern in lap joints and other joints. Small open gaps in the joints form regions where moisture can be trapped. The trapped water often produces a chemical environment in the regions which accelerates the corrosion process. Corrosion considerably reduces structural strength and load-bearing capacity of the structure. To ensure flight safety, it is imperative to detect subsurface corrosion as early as possible during aircraft maintenance operations. Since long downtimes of commercial aircraft translate to large operating costs for airline industries, it is desirable to develop techniques that can consistently and reliably detect corrosion by rapidly scanning the aircraft. Towards this goal, the thermal technique, which is a nondestructive, noncontacting technique capable of rapidly inspecting aircraft structures for defects such as disbonds, corrosion, and cracks is currently under development [1]. Also under parallel development are techniques such as ultrasonics, magneto-optics, shearography, electromagnetics, and radiography.

Keywords

Material Loss Test Region Thermal Data Loss Location Thermal Curf 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    J. S. Heyman, “NDE Research for Aging Aircraft Integrity”, Proceedings of the IEEE 1990 Ultrasonics Symposium, Honolulu, Hawaii, December 1990.Google Scholar
  2. 2.
    D. R. Prabhu and W. P. Winfree, “Automation of Disbond Detection in Aging Aircraft Through Thermal Image Processing”, Review of Progress in Quantitative Nondestructive Evaluation, Brunswick, Maine, July 1991.Google Scholar
  3. 3.
    D. R. Prabhu, P. A. Howell, H. I. Syed, and W. P. Winfree, “Application of Artificial Neural Networks to Thermal Detection of Disbonds”, Review of Progress in Quantitative Nondestructive Evaluation, Brunswick, Maine, July 1991.Google Scholar
  4. 4.
    D. E. Rumelhart, J. L. McClelland, and the PDP Research Group, “Parallel Distributed Processing - Explorations in the Microstructure of Cognition”,The MIT Press, Cambridge, MA, 1986.Google Scholar

Copyright information

© Plenum Press, New York 1993

Authors and Affiliations

  • D. R. Prabhu
    • 1
  • W. P. Winfree
    • 2
  1. 1.Analytical Services & Materials, Inc.MS 231, NASA Langley Research CenterHamptonUSA
  2. 2.Nondestructive Evaluation Sciences BranchMS 231, NASA Langley Research CenterHamptonUSA

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