Abstract
The peak identification scheme based method (three-point definition) and the spectral moments based method (spectral moment approach) are both widely used for asperity peak modeling in tribology. To discover the differences between the two methods, a great number of rough surface profile samples with various statistical distributions are first randomly generated using FFT. Then the distribution parameters of asperity peaks are calculated for the generated samples with both methods. The obtained results are compared and verified by experiment. The variation rules of the differences between the two methods with statistical characteristics of rough surfaces are investigated. To explain for the discovered differences, the assumptions by spectral moment approach that the joint distribution of surface height, slope and curvature is normal and that the height distribution of asperities is Gaussian, are examined. The results show that it is unreasonable to assume a joint normal distribution without inspecting the correlation pattern of [z], [z′] and [z′′], and that the height distribution of asperities is not exactly Gaussian before correlation length of rough surface increases to a certain extent, 20 for instance.
Highlights
Surface micro-topography has great influence on contact performances, e.g., friction and wear [1], contact fatigue [2], heat and electric conduction [3]
The well known GW model [4] and its modified versions [5, 6] have been developed based on Hertz contact mechanics, where rough surface contact is regarded as asperity contacts
2.1 Generation of Rough Surface Profile Samples Rough surfaces with Gaussian height distribution and exponential auto-correlation function (ACF) are expressed as φ(z)
Summary
Surface micro-topography has great influence on contact performances, e.g., friction and wear [1], contact fatigue [2], heat and electric conduction [3]. In consideration of the extensive application of both three-point definition and spectral moment approach, to examine their validity and to discover their differences are beneficial to effective modeling for asperity peak. To this end, a great number of rough surface profile samples with various statistical distributions are first randomly generated using FFT. +1 deviation, correlation length and high pass filtering constant of rough surface on the differences between the two methods are investigated. According to the definition of ACF and Eq (2), it gives
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