Digital filtering of surface topography: Part I. Separation of one-process surface roughness and waviness by Gaussian convolution, Gaussian regression and spline filters

Abstract Various components of surface texture are identified, namely form, waviness and roughness. Separation of these components is done by digital filtering. Gaussian regression filter that works without running-in and running-out profile fragments as well as profile spline filter was developed. The performance of conventional Gaussian digital filter was compared with those of Gaussian regression filter and spline filter. The modelled deterministic and random one-process profiles are the objects of investigation. We found that the performance of Gaussian regression filter was better than that of spline filter. Gaussian robust profile filtering technique was established. Valley suppression Rk filter was also included. These filters were compared and some of them were recommended. This paper is given in two parts. Part I focuses on the analysis of one-process surfaces. Part II discusses mainly digital filtering of stratified textures.

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