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

机译:表面形貌的数字滤波:第一部分:通过高斯卷积,高斯回归和样条滤波器分离单过程表面粗糙度和波纹度

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摘要

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.
机译:确定了表面纹理的各种成分,即形状,波纹度和粗糙度。这些组件的分离通过数字滤波完成。开发了无需导入和导出轮廓片段的高斯回归滤波器以及轮廓样条滤波器。将常规高斯数字滤波器的性能与高斯回归滤波器和样条滤波器的性能进行了比较。建模的确定性和随机单过程配置文件是研究的对象。我们发现高斯回归滤波器的性能优于样条滤波器。建立了高斯鲁棒轮廓滤波技术。谷抑制Rk滤波器也包括在内。比较了这些过滤器,并推荐了其中的一些。本文分为两部分。第一部分着重于单过程表面的分析。第二部分主要讨论分层纹理的数字滤波。

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