Loughborough University
Leicestershire, UK
LE11 3TU
+44 (0)1509 263171
Loughborough University

Loughborough University Institutional Repository

Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/20745

Title: Surface specific asperity model for prediction of friction in boundary and mixed regimes of lubrication
Authors: Leighton, Michael
Morris, Nicholas J.
Rahmani, Ramin
Rahnejat, Homer
Keywords: Real rough surfaces
Contact load carrying capacity
Issue Date: 2017
Publisher: © The Authors. Published by Springer.
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.
Abstract: 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.
Description: 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/
Sponsor: 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).
Version: Published
DOI: 10.1007/s11012-016-0397-z
URI: https://dspace.lboro.ac.uk/2134/20745
Publisher Link: http://dx.doi.org/10.1007/s11012-016-0397-z
ISSN: 0025-6455
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

Files associated with this item:

File Description SizeFormat
art%3A10.1007%2Fs11012-016-0397-z.pdfPublished version1.22 MBAdobe PDFView/Open


SFX Query

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.