Crack Sizing Using a Neural Network Classifier Trained with Data Obtained from Finite Element Models

  • Kornelija Zgonc
  • Jan D. Achenbach
  • Yung-Chung Lee
Chapter

Abstract

Ultrasonic inspection of riveted joints carried out by human operator is cumbersome and time consuming. An automated signal classification system would provide better reliability and accuracy in the determination of crack size and orientation. In this paper, we discuss a neural network designed for use in ultrasonic signal classification. The network can give classification results in a short time which makes possible real time ultrasonic inspection. An automated crack sizing system was presented earlier for similar applications [1] and the present paper is an extension of that work. The latest improvement is the use of numerically obtained ultrasonic data to train the neural network classifier (NNC).

Keywords

Attenuation 

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References

  1. 1.
    I. Komsky, K. Zgonc, and J.D. Achenbach, Review in Progress in QNDE, Vol. 13, eds. D.O Thompson and D.E Chimenti (Plenum, New York, 1994, p. 895.Google Scholar
  2. 2.
    R. Ludwig and W. Lord, “A Finite Element Formulation for the Study of Ultrasonic NDT System”, IEEE Trans. on UFFC, Vol 35, No. 6, p. 809.Google Scholar
  3. 3.
    R.H. Nielsen, Neurocomputing, Addison-Wesley Publishing Company, 1990.Google Scholar
  4. 4.
    J.D. Achenbach, Wave propagation in elastic solids, North-Holland, 1990 (sixth printing) p. 226.Google Scholar

Copyright information

© Plenum Press, New York 1995

Authors and Affiliations

  • Kornelija Zgonc
    • 1
  • Jan D. Achenbach
    • 1
  • Yung-Chung Lee
    • 1
  1. 1.Center for Quality Engineering and Failure PreventionNorthwestern UniversityEvanstonUSA

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