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Contact and Rubbing of Flat Surfaces

Archard, J. F.·1953·Journal of Applied Physics

Abstract

The interpretation of certain phenomena occuring at nominally flat surfaces in stationary or sliding contact is dependent on the assumed distribution of the real area of contact between the surfaces. Since there is little direct evidence on which to base an estimate of this distribution, the approach used is to set up a simple model and compare the deduced theory (e.g., the deduced dependence of the experimental observables on the load) with the experimental evidence. The main conclusions are as follows. (a) The electrical contact resistance depends on the model used to represent the surfaces; the most realistic model is one in which increasing the load increases both the number and size of the contact areas. (b) In general, mechanical wear should also depend on the model. However, in wear experiments showing the simplest behavior, the wear rate is proportional to the load, and these results can be explained by assuming removal of lumps at contact areas formed by plastic deformation; moreover, this particular deduction is independent of the assumed model. This suggests that a basic assumption of previous theories, that increasing the load increases the number of contacts without affecting their average size, is redundant.

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Metadata

Authors
Archard, J. F.
Venue
Journal of Applied Physics
Published
1953-08-01
Volume
24
Issue
8
Pages
981-988
ISSN
  • 0021-8979 (ISSN-L)
  • 1089-7550
OpenAlex
W1990351237
Cited by
7,892
References
0
FWCI
20.42
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Contact and Rubbing of Flat Surfaces. https://app.cochat.ai/papers/10.1063/1.1721448
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Archard, J. F. (1953). Contact and Rubbing of Flat Surfaces. Journal of Applied Physics. https://doi.org/10.1063/1.1721448

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.