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A Comparative Study on Asperity Peak Modeling Methods

Wei Zhou et al · KeAi Communications Co., Ltd · 2021

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Abstract The peak identification scheme based method (three-point definition) and the spectral moments based method (spectral moment approach) are both widely used for asperity peak modeling in tribology. To discover the differences between the two methods, a great number of rough surface profile samples with various statistical distributions are first randomly generated using FFT. Then the distribution parameters of asperity peaks are calculated for the generated samples with both methods. The obtained results are compared and verified by experiment. The variation rules of the differences between the two methods with statistical characteristics of rough surfaces are investigated. To explain for the discovered differences, the assumptions by spectral moment approach that the joint distribution of surface height, slope and curvature is normal and that the height distribution of asperities is Gaussian, are examined. The results show that it is unreasonable to assume a joint normal distribution without inspecting the correlation pattern of [z], [z′] and [z′′], and that the height distribution of asperities is not exactly Gaussian before correlation length of rough surface increases to a certain extent, 20 for instance.

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APA 7

al, W. Z. E. (2021). A Comparative Study on Asperity Peak Modeling Methods. https://doi.org/10.1186/s10033-021-00584-1

MLA

al, Wei Zhou et. "A Comparative Study on Asperity Peak Modeling Methods." 2021. https://doi.org/10.1186/s10033-021-00584-1.

Chicago

al, Wei Zhou et. 2021. "A Comparative Study on Asperity Peak Modeling Methods.". https://doi.org/10.1186/s10033-021-00584-1.

Harvard

al, W. Z. E. 2021, A Comparative Study on Asperity Peak Modeling Methods, KeAi Communications Co, Ltd, available at: https://doi.org/10.1186/s10033-021-00584-1 [Accessed 9 Aug. 2026].

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Title
A Comparative Study on Asperity Peak Modeling Methods
Author / contributors
Wei Zhou et al
Publisher
KeAi Communications Co., Ltd
Publication year
2021
ISSN
1000-9345
ISSN
1000-9345
Language
English

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