Crack’s Detection, Measuring and Counting for Resistance’s Tests Using Images

  • Carlos Briceño
  • Jorge Rivera-Rovelo
  • Narciso Acuña
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)

Abstract

Currently, material resistance research is looking for biomaterials where mechanical properties (like fatigure resistance) and biocompatibility are the main characteristics to take into account. To understand the behavior of materials subject to fatigue, usually we analyze how the material responds to cyclic forces. Failures due to fatigue are the first cause of cracks in materials. Normally, failures start with a superficial deficiency and produce micro cracks, which grow until a total break of the material. In this work we deal with the early detection of micro cracks on the surface of bone cement, while they are under fatigue tests, in order to characterize the material and design better and more resistant materials according to where they would be applied. The method presented for crack detection consists in several stages: noise reduction, shadow elimination, image segmentation and path detection for crack analysis. At the end of the analysis of one image, the number of cracks and the length of each one can be obtained (based on the maximum length of crack candidates). If a video is analyzed, the evolution of cracks in the material can be observed.

Keywords

Fatigue Crack Growth Bone Cement Crack Detection Path Detection Shadow Removal 
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.

References

  1. 1.
    Rosell, G., Mendez, J.: Bone cement: prevention of exposure to its components. Technical notes. National Institute of Health and Safety at Work. Spain (2009)Google Scholar
  2. 2.
    Quesada, F., Charris, J., Perez, J.: Ensayos de fatiga en viga rotativa para determinar la Constante de Miner del acero Aisi 1045. Prospectiva 6(2) (2008)Google Scholar
  3. 3.
    Nicholson, D., Ni, P., Ahn, Y.: Probabilistic theory for mixed mode fatigue crack growth in brittle plazes with random cracks. Engineering Fracture Mechanics 66, 305–320 (2000)CrossRefGoogle Scholar
  4. 4.
    Meyer, S., Bruckner-Foit, A., Moslang, A., Diegele, E.: Stochastic simulation model for microcracks in a martensitic steel. Computational Materials Science 26, 102–110 (2003)CrossRefGoogle Scholar
  5. 5.
    Heron, E., Walsh, C.: A continuous latent spatial model for crack initiation in bone cement. Applied Statistics 57, 25–42 (2008)MathSciNetGoogle Scholar
  6. 6.
    Chiquet, J., Limnios, N., Eid, M.: Piecewise deterministic Markov processes applied to fatigue crack growth modelling. Journal of Statistical Planning and Inference 139(5), 1657–1667 (2009)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on System, Man, and Cybernetics SMC-9(1) (1979)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Carlos Briceño
    • 1
  • Jorge Rivera-Rovelo
    • 1
  • Narciso Acuña
    • 1
  1. 1.Universidad Anahuac MayabMéxico

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