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Combinatorial Optimization A Comparative Analysis of Search Methods as Applied to Shearographic Fringe Modelling

  • Paul Clay
  • Alan Crispin
  • Sam Crossley
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1821)

Abstract

Applications of shearography in industry include the detection of strain anomalies which result when engineering components containing defects are subjected to stress. The output derived from shearographic apparatus is a fringe pattern which is used to confirm the integrity of, or characterise defects within, the component under test. A step towards the automation of the process is to convert the fringe lines into a mathematical representation that a computer can use for analysis. Modelling can be achieved by fitting B-spline curves to the fringe patterns and using a search to find a best fit. The paper compares the results of the run time performance of three search methods applied to this problem namely; discrete hill-climbing, random mutation hill-climbing and genetic algorithm.

Keywords

Genetic Algorithm Control Point Fringe Pattern Hill Climbing Intensity Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Jones R. & Wykes C. (1989) Holographic and Speckle Interferometry, 2nd Edition, Cambridge University Press, ISBN 0-521-34417-4Google Scholar
  2. 2.
    Chambard, J. P. Colon, E. Smiegielski, P. (1997) Applications of holographic and speckle interferometry in industry, Fringe 97 Conference on the automatic processing of fringe patterns, Bremen, Academic Verlag Series on Optical Metrology, pp 520–523.Google Scholar
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    Steinchen, W. Yang, L. X. Schuth, M. (1996) TV-shearography for measuring 3D-strains, Strain, May, pp 49–57.Google Scholar
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    Dewey, B. R. (1988) Computer Graphics for Engineers, Harper & Row Publishers, ISBN 0-06-041670-X.Google Scholar
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    Goldberg D.E. (1989) Genetic Algorithms in Search, Optimisation and Machine Learning, Addison Wesley Longman Inc. ISBN 0-201-15767-5.Google Scholar
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    Mitchell, M. (1996) An Introduction to Genetic Algorithms, Massachusetts Institute of Technology Press, ISBN 0-262-13316-4Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Paul Clay
    • 1
  • Alan Crispin
    • 1
  • Sam Crossley
    • 2
  1. 1.School of EngineeringLeeds Metropolitan UniversityLeeds
  2. 2.AOS Technology Ltd.Melton Mowbray

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