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Optimal Stochastic Excitation for Linear Flaw Detection in a Solid Material

  • Nesrine HouhatEmail author
  • Sébastien Ménigot
  • Tarek Boutkedjirt
  • Redouane Drai
  • Jean-Marc Girault
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)

Abstract

The field of ultrasonic nondestructive testing has known a great development during the recent years. In order to increase the flaw detection sensitivity, many improvements have been made in the equipment and the sensors technology. In the present work, the optimal command which maximizes the flaw detection is investigated experimentally. A parametric optimization consisting of finding the optimal excitation frequency which maximizes the Euclidean distance between a reference medium and a medium with a linear flaw has been obtained automatically by using the gradient descent algorithm. Moreover, the waveform excitation optimization has been considered. A set of stochastic signals have been transmitted to the medium. A closed loop optimization process based on a genetic algorithm allowed to find the optimal excitation without a priori knowledge on the shape of the signal. This optimal excitation converged to a sinusoidal pulse with the optimal frequency found by the parametric optimization.

Keywords

Optimal command Nondestructive testing Gradient descent algorithm Genetic algorithm Ultrasound 

Notes

Acknowledgment

The authors thank Dr Jean- Marc GREGOIRE (Université Franoçis Rabelais, Inserm, Imagerie et Cerveau, UMR U930, France, Tours) for his realizations, helpful discussions and advices about the experimental setup.

References

  1. 1.
    Fink, M.: Time reversal of ultrasonic fields–Part I: basic principles. IEEE Trans. Ultraso. Ferroelectr. Freq Contr. 39(5), 555–566 (1992)CrossRefGoogle Scholar
  2. 2.
    Girault, J.M., Ménigot, S.: Contrast optimization by metaheuristic for inclusion detection in nonlinear ultrasound imaging. Phys. Procedia 70, 614–617 (2015)CrossRefGoogle Scholar
  3. 3.
    Haupt, R., Haupt, S.: Practical Genetic Algorithms (2004)Google Scholar
  4. 4.
    Ménigot, S., Geryes, M., Charara, J., Girault, J.M.: Inclusion/flaw detection in ultrasound imaging through optimization of random transmitted wave. In: Acoustics 2013, New Delhi (2013)Google Scholar
  5. 5.
    Ménigot, S., Girault, J.M.: Optimization of contrast resolution by genetic algorithm in ultrasound tissue harmonic imaging. Ultrasonics 71, 231–244 (2016)CrossRefGoogle Scholar
  6. 6.
    Ménigot, S., Girault, J.M., Voicu, I., Novell, A.: Optimization of contrast-to-tissue ratio by frequency adaptation in pulse inversion imaging. IEEE Trans. Ultraso. Ferroelectr. Freq Contr. 59(11), 2431–2438 (2012)CrossRefGoogle Scholar
  7. 7.
    Widrow, B., Stearns, S.: Adaptive Signal Processing. Prentice Hall, Englewood Cliffs (1985)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nesrine Houhat
    • 1
    Email author
  • Sébastien Ménigot
    • 2
    • 3
  • Tarek Boutkedjirt
    • 4
  • Redouane Drai
    • 1
  • Jean-Marc Girault
    • 2
    • 3
  1. 1.Research Center in Industrial Technologies CRTIAlgiersAlgeria
  2. 2.Eseo GroupAngersFrance
  3. 3.LAUM, CNRS UMR 6613, Le Mans IniversitéLe MansFrance
  4. 4.Physics of Ultrasound Research Team, Faculty of PhysicsUSTHBAlgiersAlgeria

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