Skip to main content
SpringerLink
Log in

Using fuzzy sets to estimate the geometric parameters of surface damage

  • Analysis and Synthesis of Signals and Images
  • Published:
Optoelectronics, Instrumentation and Data Processing Aims and scope

Abstract

This paper considers the influence of the variable parameters of a surface defect detection algorithm on the result of its operation. A method for estimating the influence of the variable parameters on the recognized geometric characteristics of the surface defect network is proposed. This method is based on representing the basic zones of the skeleton of the surface damage network in the form of compact fuzzy sets in two-dimensional space (fuzzy quasi-points). The set of points of the recognized object obtained for various combinations of the test algorithm parameters is represented in the form of a fuzzy set with a certain membership function. A method for calculating the geometric parameters of the damage network (length and slope) by means of fuzzy geometry is considered, and its use for determining the geometric parameters of the damage network of a continuous casting roller is demonstrated.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. M. Gavilán, D. Balcones, O. Marcos, et al., “Adaptive road Crack Detection System by Pavement Classification,” Sensors 11 (10), 9628–9657 (2011).

    Article  Google Scholar 

  2. I. Konovalenko, P. Maruschak, A. Menou, et al., “A Novel Algorithm for Damage Analysis of Fatigue Sensor by Surface Deformation Relief Parameters,” in Intern. Symp. on Operational Research and Applications (ISORAP), Marrakesh, Morocco, 2013, pp. 678–684.

    Google Scholar 

  3. P. O. Maruschak, S. V. Panin, S. R. Ignatovich, et al., “Influence of Deformation Process in Material at Multiple Cracking and Fragmentation of Nanocoating,” Theor. Appl. Fracture Mech. 57 (1), 43–48 (2012).

    Article  Google Scholar 

  4. M. Bornert, F. Brémand, P. Doumalin, et al., “Assessment of Digital Image Correlation Measurement Errors: Methodology and Results,” Exp. Mech. 49 (3), 353–370 (2009).

    Article  Google Scholar 

  5. I. V. Konovalenkoand and P. O. Marushchak, “Error Analysis of an Algorithm for Identifying Thermal Fatigue Cracks,” Avtometriya 47 (4), 49–57 (2011) [Optoelectron., Instrum. Data Process. 47 (4), 360–367 (2011)].

    Google Scholar 

  6. C. M. Bishop, Pattern Recognition and Machine Learning (Springer-Verlag, New York, 2006).

    MATH  Google Scholar 

  7. P. Maruschak, V. Gliha, I. Konovalenko, et al., “Physical Regularities in the Cracking of Nanocoatings and a Method for an Automated Determination of the Crack-Network Parameters,” Materials and Technology 46 (5), 525–529 (2012).

    Google Scholar 

  8. S. V. Panin, V. V. Titkov, and P. S. Lyubutin, “Efficiency of Vector Field Filtration Algorithms in Estimating Material Strain by the Method of Digital Image Correlation,” Avtometriya 49 (2), 57–67 (2013) [Optoelectron., Instrum. Data Process. 49 (2), 155–163 (2013)].

    Google Scholar 

  9. I. V. Konovalenko and P. O. Maruschak, “Automated Method for Studying the Deformation Behavior of a Material Damaged by a Thermal Fatigue Crack Network,” Avtometriya 49 (3), 36–43 (2013) [Optoelectron., Instrum. Data Process. 49 (3), 243–249 (2013)].

    Google Scholar 

  10. J. N. Mordeson and P. S. Nair, Fuzzy Mathematics: An Introduction for Engineers and Scientists (Verlag, Heidelberg, 2001).

    Book  MATH  Google Scholar 

  11. J. J. Buckley and E. Eslami, “Fuzzy Plane Geometry. I: Points and Lines,” Fuzzy Sets Syst. 86 (2), 179–187 (1997).

    Article  MathSciNet  MATH  Google Scholar 

  12. E. L. Kuleshov, “Goodness-of-Fit Test Based on the Interval Estimation,” Avtometriya 52 (1), 30–36 (2016) [Optoelectron., Instrum. Data Process. 52 (1), 24–29 (2016)].

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Ivan Pulyui Ternopil National Technical University, ul. Russkaya 56, Ternopil, 46001, Ukraine

    I. V. Konovalenko, O. A. Pastukh & P. O. Marushchak

Authors
  1. I. V. Konovalenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. O. A. Pastukh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. O. Marushchak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. O. Marushchak.

Additional information

Original Russian Text © I.V. Konovalenko, O.A. Pastukh, P.O. Marushchak, 2016, published in Avtometriya, 2016, Vol. 52, No. 4, pp. 3–13.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Konovalenko, I.V., Pastukh, O.A. & Marushchak, P.O. Using fuzzy sets to estimate the geometric parameters of surface damage. Optoelectron.Instrument.Proc. 52, 319–327 (2016). https://doi.org/10.3103/S8756699016040014

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S8756699016040014

Keywords

Search

Navigation