Abstract
Structured surfaces enhance the functionality of components. Well known is the influence of the surface structure on friction and wear behavior. Beyond this, structured surfaces are widely used for various purposes such as optical, biological or mechanical applications. Therefore, the characterization of structured surfaces and surface features becomes increasingly important. The functionality of a surface can either be tested directly or indirectly. Due to the correlation of geometric surface features and its functionality, an indirect and self-evident way is by measuring the surface topography. To obtain the geometric essentials of these features, they need to be separated from the raw surface data. The standard procedure of decomposing a surface topography is by the use of a Gaussian filter bank, gaining so called scale-limited surfaces. This procedure shows drawbacks when characterizing structured surfaces by introducing distortions to the feature boundaries. To overcome these limitations, this work proposes the use of an automatic nonlinear anisotropic diffusion filter as an initial step to separate the features from the residual surface topography because of its edge preserving properties. It is shown that the nonlinear anisotropic diffusion serves well the separation of the features and their geometrical characterization.
Highlights
It is widely known that structured surfaces serve the functionality of components
Due to the correlation of geometric surface features and its functionality, an indirect and selfevident way is by measuring the surface topography
The standard procedure of decomposing a surface topography is by the use of a Gaussian filter bank, gaining so called scale-limited surfaces
Summary
It is widely known that structured surfaces serve the functionality of components. Well-known functions are of optical or mechanical nature like the Fresnel lens or the influence of surface features on friction and wear behavior. Evans et al stated already in 1999 that the function of structured surfaces cannot be related to traditional surface finishing parameters [2]. Mathia et al showed more recently that the traditional concept of determined parameters for roughness and waviness does not satisfy the characterization of structured surfaces [1]. Scalelimited surface descriptions are most widely gained by spatial linear Gaussian filtering with determined cutoff frequencies. For structured surfaces this holds some drawbacks. Filtering the surface data with a linear Gaussian filter can diminish slopes at crucial
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