Defect quantification with reference-free thermal contrast and artificial neural networks

Hernan D. Benitez; Clemente Ibarra-Castanedo; AbdelHakim Bendada; Xavier Maldague; Humberto Loaiza; Eduardo Caicedo

doi:10.1117/12.718272

9 April 2007 Defect quantification with reference-free thermal contrast and artificial neural networks

Hernan D. Benitez, Clemente Ibarra-Castanedo, AbdelHakim Bendada, Xavier Maldague, Humberto Loaiza, Eduardo Caicedo

Proceedings Volume 6541, Thermosense XXIX; 65410V (2007) https://doi.org/10.1117/12.718272
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

The Infrared Nondestructive Testing (IRNT) methods based on thermal contrast are strongly affected by non-uniform heating at the surface. Hence, the results obtained from these methods considerably depend on the chosen reference point. One of these methods is Artificial Neural Networks (ANN) that uses thermal contrast curves as input data for training and test in order to detect and estimate defect depth. The Differential Absolute Contrast (DAC) has been successfully used as an alternative thermal contrast to eliminate the need of a reference point by defining the thermal contrast with respect to an ideal sound area. The DAC technique has been proven effective to inspect materials at early times since it is based on the 1D solution of the Fourier equation. A modified DAC version using thermal quadrupoles explicitly includes the sample thickness in the solution, extending in this way the range of validity when the heat front approaches the sample rear face. We propose to use ANN to detect and quantify defects in composite materials using data extracted from the modified DAC with thermal quadrupoles in order to decrease the non-uniform heating and plate shape impact on the inspection.

Citation Download Citation

Hernan D. Benitez, Clemente Ibarra-Castanedo, AbdelHakim Bendada, Xavier Maldague, Humberto Loaiza, and Eduardo Caicedo "Defect quantification with reference-free thermal contrast and artificial neural networks", Proc. SPIE 6541, Thermosense XXIX, 65410V (9 April 2007); https://doi.org/10.1117/12.718272

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