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Surface specific asperity model for prediction of friction in boundary and mixed regimes of lubrication

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journal contribution
posted on 2016-03-31, 13:36 authored by Michael Leighton, Nick MorrisNick Morris, Ramin RahmaniRamin Rahmani, Homer Rahnejat
Machine downsizing, increased loading and better sealing performance have progressively led to thinner lubricant films and an increased chance of direct surface interaction. Consequently, mixed and boundary regimes of lubrication are prevalent with ubiquitous asperity interactions, leading to increased parasitic losses and poor energy inefficiency. Surface topography has become an important consideration as it influences the prevailing regime of lubrication. As a result a plethora of machining processes and surface finishing techniques have emerged. The stochastic nature of the resulting topography determines the separation at which asperity interactions are initiated and ultimately affect the conjunctional load carrying capacity and operational efficiency. The paper presents a procedure for modelling of asperity interactions of real rough surfaces, from measured data, which do not conform to the usually assumed Gaussian distributions. The model is validated experimentally using a bench top reciprocating sliding test rig. The method demonstrates accurate determination of the onset of mixed regime of lubrication. In this manner, realistic predictions are made for load carrying and frictional performance in real applications where commonly used Gaussian distributions can lead to anomalous predictions.

Funding

The authors would like to thank the UK Engineering and Physical Sciences Research Council (EPSRC) for the sponsorship of this research under the Encyclopaedic Program Grant (www.encyclopaedic.org).

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Meccanica

Pages

1 - 13

Citation

LEIGHTON, M. ...et al., 2017. Surface specific asperity model for prediction of friction in boundary and mixed regimes of lubrication. Meccanica, 52 (1), pp. 21-33.

Publisher

© The Authors. Published by Springer.

Version

  • VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/ by/4.0/

Publication date

2017

Notes

This is an Open Access Article. It is published by Springer under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

ISSN

0025-6455

eISSN

1572-9648

Language

  • en

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