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A Multiscale Statistical Analysis of Rough Surfaces and Applications to Tribology

Author

Listed:
  • Feodor M. Borodich

    (College of Aerospace Engineering, Chongqing University, Chongqing 400044, China
    These authors contributed equally to this work.)

  • Andrey Pepelyshev

    (School of Mathematics, Cardiff University, Senghennydd Road, Cardiff CF24 4AG, UK
    These authors contributed equally to this work.)

  • Xiaoqing Jin

    (College of Aerospace Engineering, Chongqing University, Chongqing 400044, China
    These authors contributed equally to this work.)

Abstract

Mathematical modelling of surface roughness is of significant interest for a variety of modern applications, including, but not limited to, tribology and optics. The most popular approaches to modelling rough surfaces are reviewed and critically examined. By providing counterexamples, it is shown that approaches based solely on the use of the fractal geometry or power spectral density have many drawbacks. It is recommended to avoid these approaches. It is argued that the surfaces that cannot be distinguished from the original rough surfaces can be synthesised by employing the concept of the representative elementary pattern of roughness (REPR), i.e., the smallest interval (or area) of a rough surface that statistically represents the whole surface. The REPR may be extracted from surface measurement data by the use of the “moving window” technique in combination with the Kolmogorov–Smirnov statistic.

Suggested Citation

  • Feodor M. Borodich & Andrey Pepelyshev & Xiaoqing Jin, 2024. "A Multiscale Statistical Analysis of Rough Surfaces and Applications to Tribology," Mathematics, MDPI, vol. 12(12), pages 1-19, June.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:12:p:1804-:d:1412063
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    References listed on IDEAS

    as
    1. S. Davies & P. Hall, 1999. "Fractal analysis of surface roughness by using spatial data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 3-37.
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