NASA Logo

NTRS

NTRS - NASA Technical Reports Server

Back to Results
Application of artificial neural networks to thermal detection of disbondsA novel technique for processing thermal data is presented and applied to simulation as well as experimental data. Using a neural network of thermal data classification, good classification accuracies are obtained, and the resulting images exhibit very good contrast between bonded and disbonded locations. In order to minimize the preprocessing required before using the network of classification, the temperature values were directly employed to train a network using data from an on-site testing run of a commercial aircraft. Training was extremely fast, and the resulting classification also agreed reasonably well with an ultrasonic characterization of the panel. The results obtained using one sample show the partially disbonded vertical doubler. The vertical lines along the doubler correspond to the original extent of the doubler obtained using blueprints of the aircraft.
Document ID
19930035602
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Prabhu, D. R.
(NASA Langley Research Center Hampton, VA, United States)
Howell, P. A.
(NASA Langley Research Center Hampton, VA, United States)
Syed, H. I.
(Analytical Services and Materials, Inc.; NASA, Langley Research Center Hampton, VA, United States)
Winfree, W. P.
(NASA Langley Research Center Hampton, VA, United States)
Date Acquired
August 15, 2013
Publication Date
January 1, 1992
Publication Information
Publication: In: Review of progress in quantitative nondestructive evaluation. Vol. 11B; Proceedings of the 18th Annual Review, Brunswick, ME, July 28-Aug. 2, 1991 (A93-19582 06-38)
Publisher: Plenum Press
Subject Category
Quality Assurance And Reliability
Accession Number
93A19599
Funding Number(s)
CONTRACT_GRANT: NAS1-18599
Distribution Limits
Public
Copyright
Other

Available Downloads

There are no available downloads for this record.
No Preview Available