Two important aspects of surface engineering are measurement and analysis. While the acquisition of 3-D surface data is largely improved by optical profilometry, there has also been much research about their evaluation. Although the fractal-based or multiscale analysis of surface roughness is well known, these methods are not tailored for the characterization of the roughness of important machined surface textures produced by turning, grinding, honing, etc. In this work, a multiscale model of rough groove textures for 3-D surface measurements is introduced. Under the assumption of fractal groove profiles, the groove textures are modeled as superpositions of orthogonal ridge functions by the ridgelet packet transform. This provides a compact representation of groove textures. This allows a restoration of roughness components from accurate optical measurements, so that precise characterization can be achieved based on restoration. This method is validated for restoration of honing textures for real optical surface measurements on a cylinder liner.
Machine work pieces with ground, broached or milled surfaces have frequently microtextures consisting of stochastically
placed straight tool marks. In this paper we'll exploit the depth data acquired by white-light interferometer
for the surface analysis. We present a new algorithm for efficiently extracting extensive groove bands from depth
images. The images are treated as a composition of shape component, straight line structures and background.
The cylindrical shape component is extracted using robust least squares methods. The outliers are removed by
adaptive center weighted median filter. The undefined regions due to sensor failures are interpolated using successive
over-relaxation algorithm. An algorithm for the separation of groove bands is introduced. Straight line in
three-dimensional space is parameterized and used as primitive for groove separation. After having determined
the orientation of the groove band, straight line segment produced with Digital Differential Analyzer can be used
by the scanning algorithm for estimating the straight lines. This decomposition enables a separate evaluation of
different components of the surface data. The results of the pre-processings and the separation turn out to be
fast and robust, which is verified by real depth data.
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