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Research Papers: Contact Mechanics

A Simulation Method for Non-Gaussian Rough Surfaces Using Fast Fourier Transform and Translation Process Theory

[+] Author and Article Information
Yuechang Wang

State Key Laboratory of Tribology,
Tsinghua University,
Beijing 100084, China
e-mail: skywalker_yc@163.com

Ying Liu

State Key Laboratory of Tribology,
Tsinghua University,
Beijing 100084, China
e-mail: liuying@mail.tsinghua.edu.cn

Gaolong Zhang

State Key Laboratory of Tribology,
Tsinghua University,
Beijing 100084, China
e-mail: gl-zh14@mails.tsinghua.edu.cn

Yuming Wang

State Key Laboratory of Tribology,
Tsinghua University,
Beijing 100084, China
e-mail: yumingwang@mail.tsinghua.edu.cn

1Corresponding author.

Contributed by the Tribology Division of ASME for publication in the JOURNAL OF TRIBOLOGY. Manuscript received January 18, 2017; final manuscript received August 5, 2017; published online October 4, 2017. Assoc. Editor: Liming Chang.

J. Tribol 140(2), 021403 (Oct 04, 2017) (10 pages) Paper No: TRIB-17-1026; doi: 10.1115/1.4037793 History: Received January 18, 2017; Revised August 05, 2017

The simulated rough surface with desired parameters is widely used as an input for the numerical simulation of tribological behavior such as the asperity contact, lubrication, and wear. In this study, a simulation method for generating non-Gaussian rough surfaces with desired autocorrelation function (ACF) and spatial statistical parameters, including skewness (Ssk) and kurtosis (Sku), was developed by combining the fast Fourier transform (FFT), translation process theory, and Johnson translator system. The proposed method was verified by several numerical examples and proved to be faster and more accurate than the previous methods used for the simulation of non-Gaussian rough surfaces. It is convenient to simulate the non-Gaussian rough surfaces with various types of ACFs and large autocorrelation lengths. The significance of this study is to provide an efficient and accurate method of non-Gaussian rough surfaces generation to numerically simulate the tribological behavior with desired rough surface parameters.

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References

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Figures

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Fig. 1

Flowchart of the proposed method

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Fig. 2

Effects of α on the Ssk and Sku values of the simulated rough surfaces. Target Ssk = −0.5 Sku = 4.83.

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Fig. 3

Effects of α on the mean ACF of the simulated rough surfaces

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Fig. 4

Simulated non-Gaussian rough surfaces and corresponding histogram. Target Ssk = −0.5; Sku = 4.83.

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Fig. 5

Comparison of the mean ACF value of the simulated surfaces and the target ACF 1: (a) x direction and (b) y direction

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Fig. 6

Ssk values of the simulated surfaces with the target ACF 1

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Fig. 7

Sku values of the simulated surfaces with the target ACF 1

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Fig. 8

Performance comparison of Hu's, Minet's, and the proposed method with parameter group (1,1) and the target ACF 1

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Fig. 9

Performance comparison of Hu's, Minet's, and the proposed method with parameter group (1,3) and the target ACF 1

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Fig. 10

Comparison of the mean ACF value of the simulated surfaces and the target ACF 2: (a) x direction and (b) y direction

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Fig. 11

Ssk values of the simulated surfaces with the target ACF 2

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Fig. 12

Sku values of the simulated surfaces with the target ACF 2

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