Neural network based processing of thermal NDE data for corrosion detection

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1993
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Prabhu, D.
Winfree, W.
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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.

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Fri Jan 01 00:00:00 UTC 1993