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Research Papers: Contact Mechanics

Research on the Obtainment of Topography Parameters by Rough Surface Simulation With Fast Fourier Transform

[+] Author and Article Information
Yan Fei He

School of Mechanical and Electrical Engineering,
Central South University,
Changsha, Hunan 410083, China
e-mail: 627461220@qq.com

Jin Yuan Tang

School of Mechanical and Electrical Engineering,
Central South University,
Changsha, Hunan 410083, China
e-mail: jytangcsu@163.com

Wei Zhou

School of Mechanical and Electrical Engineering,
Central South University,
Changsha, Hunan 410083, China
e-mail: cnihelat@163.com

Dong Ri Liao

School of Mechanical and Electrical Engineering,
Central South University,
Changsha, Hunan 410083, China
e-mail: xdn666@126.com

1Corresponding author.

Contributed by the Tribology Division of ASME for publication in the JOURNAL OF TRIBOLOGY. Manuscript received July 29, 2014; final manuscript received February 14, 2015; published online April 6, 2015. Assoc. Editor: Robert L. Jackson.

J. Tribol 137(3), 031401 (Jul 01, 2015) (7 pages) Paper No: TRIB-14-1191; doi: 10.1115/1.4029843 History: Received July 29, 2014; Revised February 14, 2015; Online April 06, 2015

Asperity radius of curvature and asperity density, which are generally obtained from rough surface simulation with fast Fourier transform (FFT), are the two essential parameters for statistical contact model. In simulation, however, the value of a parameter (defined as “autocorrelation function (ACF) truncation length” in this paper), which is arbitrarily chosen and has been paid little attention to in most relevant literature, is found to have a great effect on topography parameters, regardless of the methods chosen to calculate them. Improper determination of the ACF truncation length may induce erroneous results. This paper points out how to make the proper determination of the ACF truncation length to guarantee a certain degree of precision and explains why improper determination of the ACF truncation length may cause serious errors when calculating the topography parameters. Besides, the asperity radius of curvature and the asperity density of the generated rough surfaces are calculated using the eight-summit identification method, and their formulae in terms of correlation length are obtained through numerical fitting.

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References

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Figures

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Fig. 1

Generated isotropic Gaussian rough surface with sample size 512 × 512

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Fig. 2

Comparison of ACF in the x direction (note: correlation length β* = 8, ACF truncation length T = 26)

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Fig. 3

Changes of asperity density with correlation length (note: ACF truncation length is taken as different constants, and asperity density is calculated using eight-summit identification method)

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Fig. 4

Changes of asperity radius of curvature with correlation length (note: ACF truncation length is taken as different constants, and asperity radius of curvature is calculated using eight-summit identification method)

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Fig. 5

Changes of asperity density with correlation length (note: ACF truncation length is taken as different constants, asperity density is calculated using eight-summit identification method, and specimen surface is 1024 × 1024)

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Fig. 6

Changes of asperity density with correlation length (note: ACF truncation length is taken as different constants, and asperity density is calculated using spectral moments approach)

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Fig. 7

Changes of asperity radius of curvature with correlation length (note: ACF truncation length is taken as different constants, and asperity radius of curvature is calculated using spectral moments approach)

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Fig. 8

Changes of asperity density with correlation length (note: ACF truncation length is taken as different times of correlation length, and asperity density is calculated using eight-summit identification method)

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Fig. 9

Changes of asperity radius of curvature with correlation length (note: ACF truncation length is taken as different times of correlation length, and asperity radius of curvature is calculated using eight-summit identification method)

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Fig. 10

Comparison of ACFs with different ACF truncation lengths (note: correlation length is equal to 70)

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Fig. 11

Effect of ACF truncation length on mean value of height

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Fig. 12

Effect of ACF truncation length on root mean square deviation

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Fig. 13

Effect of ACF truncation length on skewness

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Fig. 14

Effect of ACF truncation length on kurtosis

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Fig. 15

Comparison of the primitive and the fitting asperity radius of curvature as a function of the correlation length

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Fig. 16

Comparison of the primitive and the fitting asperity density

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