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.
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