4.7 Article

The influence of the statistical properties of self-affine surfaces in elastic contacts: A numerical investigation

Journal

JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS
Volume 60, Issue 5, Pages 973-982

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jmps.2012.01.006

Keywords

Self-affine rough surfaces; Adhesionless contact; Fractal dimension

Funding

  1. Regione Apulia (Italy) [28]

Ask authors/readers for more resources

In the last years, an increasing number of papers has been published in the field of contact mechanics between rough fractal surfaces. The increase in research is motivated by the wide variety of natural and industrial processes that involve formation of rough surfaces and interfaces, characterized by self-similarity or self-affine properties on multiple scales. In this paper, the contact between a linear elastic half-space and a rough self-affine fractal rigid surface is studied by employing a numerical method recently developed by the authors (Putignano et al., 2012). The paper aims at investigating the influence of surface parameters as fractal dimensions, mean square slope and mean square roughness on the relation between the contact area, the load and the average separation. The results show that, for relatively small loads, the real contact area-load relationship coefficient of proportionality kappa takes the universal value kappa = 2 independent of the statistical properties and fractal dimension D-f of the rough surface. This universal constant is just in between the two values predicted respectively by Bush et al. (1975) and Persson (2001). We also find that the average separation vs. load relation is affected by the fractal dimension D-f of the rough surface, as higher D-f lead to an increase of the average separation. Finally, in this work, we also study the behavior of the power spectral densities of the elastically deformed surface and of the distribution of local separations. We find that the trend of this quantities is in agreement with recent theoretical predictions. (C) 2012 Elsevier Ltd. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available