Modeling of rough surfaces with given roughness parameters

Abstract

Modeling of rough surfaces with given roughness parameters is studied, where surfaces with Gaussian height distribution and exponential auto-correlation function (ACF) are concerned. A large number of micro topography samples are randomly generated first using the rough surface simulation method with FFT. Then roughness parameters of the simulated roughness profiles are calculated according to parameter definition, and the relationship between roughness parameters and statistical distribution parameters is investigated. The effects of high-pass filtering with different cut-off lengths on the relationship are analyzed. Subsequently, computing formulae of roughness parameters based on standard deviation and correlation length are constructed with mathematical regression method. The constructed formulae are tested with measured data of actual topographies, and the influences of auto-correlation variations at different lag lengths on the change of roughness parameter are discussed. The constructed computing formulae provide an approach to active modeling of rough surfaces with given roughness parameters.

Keywords

micro topography / rough surface / roughness / active modeling

References

[1] NovovicD, DewesR C, AspinwallD K, VoiceW, BowenP. The effect of machined topography and integrity on fatigue life [J]. Int J Mach Tool Manu, 2004, 44(2): 125-134

[2] FonteM, RomeiroF, FreitasM. Environment effects and surface roughness on fatigue crack growth at negative R-ratios [J]. Int J Fatigue, 2007, 29(9): 1971-1977

[3] GadelmawlaE S, KouraM M, MaksoudT, MA, ElewaI M, SolimanH H. Roughness parameters[J]. J Mater Process Tech, 2002, 123(1): 133-145

[4] ASME. Y14.36m-1996 surface texture symbols [S]. New York: Amer Society of Mechanical, 1996.

[5] HubertC, KubiakK J, BigerelleM, DuboisA, DubarL. Identification of lubrication regime on textured surfaces by multi-scale decomposition [J]. Tribol Int, 2015, 82: 375-386

[6] NayakP R. Random process model of rough surfaces [J]. J Lubrication Tech, 1971, 93(3): 398-407

[7] ZhouW, TangJ-y, HeY-fei. Formulae of roughness peak distribution parameters with standard deviation and correlation length [J]. Proc Inst Mech Eng Part J J Eng Tribol, 2015, 229(12): 1395-1408

[8] ManeshK, RamamoorthyB, SingaperumalM. Numerical generation of anisotropic 3D non-Gaussian engineering surfaces with specified 3D surface roughness parameters [J]. Wear, 2010, 268(1112): 1371-1379

[9] ThomasT RRough surfaces [M], 1999

[10] PawarG, PawlusP, EtsionI, RaeymaekersB. The effect of determining topography parameters on analyzing elastic contact between isotropic rough surfaces [J]. ASME J Tribol, 2013, 135: 011401

[11] WuJ J. Simulation of non-Gaussian surfaces with FFT [J]. Tribol Int, 2004, 37(4): 339-346

[12] HuY Z, TonderK. Simulation of 3-D random rough surface by 2-D digital filter and Fourier analysis [J]. Int J Mach Tool Manu, 1992, 32(12): 83-90

[13] WhitehouseD J, ArchardJ F. The properties of random surfaces of significance in their contact [J]. Proc R Soc London Ser A Math Phys, 1970, 316(1524): 97-121

[14] HirstW, HollanderA E. Surface finish and damage in sliding [J]. Proc R Soc London Ser A Math Phys, 1974, 337(1610): 379-394

[15] AramakiH, ChengH S, ChungY W. The contact between rough surfaces with longitudinal texture. I: Average contact pressure and real contact area [J]. ASME J Tribol, 1993, 115(3): 419-424