Depth Evaluation in Pulsed Phase Thermography with Neural Network

  • Y. Largouët
  • A. Darabi
  • X. Maldague
Chapter
Part of the Review of Progress in Quantitative Nondestructive Evaluation book series (RPQN, volume 18 A)

Abstract

Pulsed phase thermography (PPT) was recently introduced [1] and up to now analysis of this infrared thermographie approach for non destructive evaluation has been limited to qualitative aspects. The study presented in this paper uses a neural network (NN) approach to extract quantitative information from PPT results. Phase behavior with frequency is first analyzed with a theoretical model which is of further interest to establish the NN architecture, finally results are presented.

Keywords

Plastics 

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References

  1. 1.
    X. Maldague, S. Marinetti, “Puise Phase Infrared Thermography,” J. Appl. Phys. 79[5]: 2694–2698 (1996)Google Scholar
  2. 2.
    X. Maldague Non destructive evaluation of materials by infrared thermography (Springer Verlag, London, 1993).CrossRefGoogle Scholar
  3. 3.
    G Busse, “Nondestructive evaluation of polymer materials,” NDT &E Int’l, 27, 253 (1994)CrossRefGoogle Scholar
  4. 4.
    G. Busse, D. Wu, W. Karpen, “Thermal wave imaging with phase sensitive modulated thermography,” J. Appl Phys., 71(8): 3962–3965 (1992)ADSCrossRefGoogle Scholar
  5. 5.
    X. Maldague, J.-P. Couturier, “Review of Pulsed Phase Thermography,” IV Advanced Infrared Technology and Applications Workshop, L.R. Abbozzo, G.M. Carlomagno, C. Corsi eds, Atti della Fondazione G. Ronchi, Firenze, 1997. [in press, 1998]Google Scholar
  6. 6.
    Favro L.D., Han X., “Thermal Wave Materials Characterization and Thermal Wave Imaging,” ASNT Advanced School on Sensors for Process Monitoring and Quality Control, American Soc. for NonDestructive Testing Press, [in press, 1998]Google Scholar
  7. 7.
    Largouët Y., “Rapport de stage sur la thermographie de phase puisée, ”Université Laval, Laboratoire de Vision et Systèmes Numériques, 77 p., Sept. 1997. [in French]Google Scholar
  8. 8.
    J.W. Maclachlan Spicer, W.D. Kerns, L.C. Aamodt, J.C. Murphy, “Time-resolved infrared radiometry (TRIR) of multilayer organic coatings using surface and subsurface heating” in Thermosense XIII, Proc. SPIE, G. S. Bairded., 1467: 311–321, 1991.ADSCrossRefGoogle Scholar
  9. 9.
    Prabhu, D. R., Howell, P. A., Syed, H. I., Winfree, W. P., “Application of artificial neural networks to thermal detection of disbonds,” Review of Progress in Quantitative NDE, D.O. Thompson, D.E. Chimenti eds, 11B: 1331–13382 (Plenum Press, 1992).Google Scholar
  10. 10.
    Bison P.G., Bressan C., Di Sarno R., Grinzato E., Marinetti S., Manduchi G, “Thermal NDE of delaminations in plastics by neural network processing” in Proc. of Quantitative Infrared Thermography, Sorrento, Italy (QIRT 94, Eurotherm Seminar 42, Éd. Eur. Thermique et Industrie), D. Balageas, G Busse, G.M. Carlomagno eds, 214–219, 1994.Google Scholar
  11. 11.
    Santey M.B., Almond D. P., “An artificial neural network interpreter for transient thermography image data”, 11B NDT & E Int., 30(5): 291–295, 1997.CrossRefGoogle Scholar
  12. 12.
    Lau C. (ed.) Neural Networks, IEEE Press, 327 pp., 1991Google Scholar

Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • Y. Largouët
    • 1
  • A. Darabi
    • 1
  • X. Maldague
    • 1
  1. 1.Electrical and Computing Eng. DeptUniversité LavalQuebec CityCanada

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