Boundary Interactions of Rough non-Gaussian Surfaces

Surface topography is important as it influences contact load-carrying capacity and operational efficiency through generated friction, as well as wear. As a result, a plethora of machining processes and surface finishing techniques have been developed. These processes yield topographies, which are often non-Gaussian, with roughness parameters that alter hierarchically according to their interaction heights. They are also subject to change through processes of rapid initial running-in wear as well as any subsequent gradual wear and embedding. The stochastic nature of the topography makes for complexity of contact mechanics of rough surfaces, which was first addressed by the pioneering work of Greenwood and Williamson, which among other issues is commemorated by this contribution. It is shown that their seminal contribution, based on idealised Gaussian topography and mean representation of asperity geometry should be extended for practical applications where surfaces are often non-Gaussian, requiring the inclusion of surface-specific data which also evolve through process of wear. The paper highlights a process dealing with practical engineering surfaces from laboratory-based testing using a sliding tribometer to accelerated fired engine testing for high performance applications of cross-hatched honed cylinder liners. Such an approach has not hitherto been reported in literature

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

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