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Research Papers: Friction & Wear

Application of Elastic-Plastic Static Friction Models to Rough Surfaces With Asymmetric Asperity Distribution

[+] Author and Article Information
Chul-Hee Lee1

Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801

Melih Eriten

Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801

Andreas A. Polycarpou2

Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801polycarp@illinois.edu

1

Present address: Department of Mechanical Engineering, INHA University, 253 Yonghyun-Dong, Nam-Gu, Incheon 402-751, South Korea.

2

Corresponding author.

J. Tribol 132(3), 031602 (Jun 15, 2010) (11 pages) doi:10.1115/1.4001547 History: Received November 20, 2009; Revised March 16, 2010; Published June 15, 2010

Asymmetric height distribution in surface roughness is important in many engineering surfaces, such as in constant velocity (CV) joints, where specific manufacturing processes could result in such surfaces. Even if the initial surfaces exhibit symmetric roughness, the running-in and sliding processes could result in asymmetric roughness distributions. In this paper, the effect of asymmetric asperity height distribution on the static friction coefficient is investigated theoretically and experimentally. The asymmetry of the surface roughness is modeled using the Pearson system of frequency curves. Two elastic-plastic static friction models, the Kogut–Etsion (KE) and Cohen–Kligerman–Etsion (CKE) models are adapted to account for asymmetric roughness and employed to obtain the tangential and normal contact forces. Static friction experiments using CV joint roller and housing surfaces, which exhibit different levels of surface roughness, were performed and directly compared with the KE and CKE static friction models using both a symmetric Gaussian as well as Pearson distributions of asperity heights. It is found that the KE model with the Pearson distribution compares favorably with the experimental measurements.

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Copyright © 2010 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Surface roughness profiles (1000×100 μm2 scan of surface heights) and microscopic images for the roller and housing

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Figure 2

Surface heights profile measurements showing the changes of surface roughness under different running-in conditions (dashed lines designate one standard deviation)

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Figure 3

Changes of plasticity index for the individual and combined rough surfaces

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Figure 4

Surface height roughness histogram for the housing with Pearson and Gaussian distribution fits: (a) virgin, (b) stabilized, and (c) worn surfaces

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Figure 5

Probability density functions generated using the Pearson method for different running-in conditions

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Figure 6

Contacting nominally flat rough interface showing relevant interfacial forces

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Figure 7

KE model friction forces acting between contacting rough surfaces for different running-in conditions with Pearson and Gaussian distributions

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Figure 8

Static friction coefficient versus contact load using the CKE and KE elastic-plastic static friction models with Pearson distribution under different running-in conditions

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Figure 9

Schematic control diagram for the static friction measurement test apparatus

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Figure 10

Comparison of the KE model’s estimation of static friction coefficients and experimental results (both dry and lubricated conditions) under different sliding velocities (for stabilized surface). CKE model predictions (not shown for clarity) are 0.51 and 0.65 for Gaussian and Pearson, respectively.

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Figure 11

Comparison of the CKE and KE models’ estimation of static friction coefficient with Pearson distribution and experimental results under different running-in conditions

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