Correlating motif analysis and morphological filters for surface texture analysis

Lou, Shan; Jiang, Xiangqian; Scott, Paul J.

doi:10.1016/j.measurement.2012.10.001

Cited by 25 publications

(15 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Motif analysis was successful in characterizing functional properties of surfaces, especially in friction and contact problems [12][13][14]. In those studies, parameters were used both to identify and separate anisotropic components by appropriate anisotropic filtering and characterization of surface motifs.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Characterizations of Surface Anisotropies

2020

View full text Add to dashboard Cite

Anisotropy can influence surface function and can be an indication of processing. These influences and indications include friction, wetting, and microwear. This article studies two methods for multiscale quantification and visualization of anisotropy. One uses multiscale curvature tensor analysis and shows anisotropy in horizontal coordinates i.e., topocentric. The other uses multiple bandpass filters (also known as sliding bandpass filters) applied prior to calculating anisotropy parameters, texture aspect ratios (Str) and texture directions (Std), showing anisotropy in horizontal directions only. Topographies were studied on two milled steel surfaces, one convex with an evident large scale, cylindrical form anisotropy, the other nominally flat with smaller scale anisotropies; a µEDMed surface, an example of an isotropic surface; and an additively manufactured surface with pillar-like features. Curvature tensors contain the two principal curvatures, i.e., maximum and minimum curvatures, which are orthogonal, and their directions, at each location. Principal directions are plotted for each calculated location on each surface, at each scale considered. Histograms in horizontal coordinates show altitude and azimuth angles of principal curvatures, elucidating dominant texture directions at each scale. Str and Std do not show vertical components, i.e., altitudes, of anisotropy. Changes of anisotropy with scale categorically failed to be detected by traditional characterization methods used conventionally. These multiscale methods show clearly in several representations that anisotropy changes with scale on actual surface measurements with markedly different anisotropies.

show abstract

Section: Introductionmentioning

confidence: 99%

Multiscale Characterizations of Surface Anisotropies

2020

View full text Add to dashboard Cite

show abstract

“…• its characteristic depth T, which is the lower depth of this two (� � �� , �� . Fragments received this way are next joined according to specific rules among which following conditions are distinguished: envelope, length, magnification and equality [5,6].…”

Section: Motifs Methodsmentioning

confidence: 99%

Possibilities of applying motif group parameters for description roughness of turned surfaces

et al. 2018

View full text Add to dashboard Cite

This article explores the methodology of assessing the applicability of roughness parameters from the motif group to the evaluation of one-way and periodic geometrical surface structures. The results of surface roughness measurements of aluminum samples turned with variable kinematic parameters were presented. Usability of using surface motifs in combination with selected parameters described in ISO standards for assessment of geometrical structures characteristic for longitudinal turning was shown.

show abstract

“…Morphological filters are other types of nonlinear filters that can be used to suppress the outliers in measurement data. By moving spherical or flat structuring elements across measurement data, morphological closing operation can remove the pierces, and in opposite morphological opening operation can suppress the spikes [35]. Podulka et al [36] presented an example of using a morphological closing filter with two sizes of flat structuring element to detect the spikes on a cylinder liner profile.…”

Section: B Post-process Approachesmentioning

confidence: 99%

Application of Clustering Filter for Noise and Outlier Suppression in Optical Measurement of Structured Surfaces

Lou

Tang

Zeng

et al. 2020

IEEE Trans. Instrum. Meas.

Self Cite

View full text Add to dashboard Cite

In comparison to tactile sensors, optical techniques can provide a fast, non-destructive profile/areal surface measurement solution. Nonetheless, high measurement noise, unmeasured points and outliers, are often observed in optical measurement, particularly for structured surfaces. To alleviate their detrimental impacts on the characterization of surface topography as well as the examination of micro/nanoscale geometries, a post-processing filtering technique, i.e. the clustering filter, which is essentially an iterative process to find the aggregation center of a cluster of points, is implemented. The clustering filter is particularly useful for noises and outlier suppression for optical measurement of structured surfaces due to its edge-preserving capability. Five surface samples with structured features are measured by an in-house developed dispersive interferometer and a commercial white light interferometer, thereafter the measured surface data is filtered by the clustering filter. Both noise and outliers are suppressed, which not only facilitates the visualization and characterization of surface topography, but also enables the accurate evaluation of local functional geometries.

show abstract

Correlating motif analysis and morphological filters for surface texture analysis

Cited by 25 publications

References 22 publications

Multiscale Characterizations of Surface Anisotropies

Multiscale Characterizations of Surface Anisotropies

Possibilities of applying motif group parameters for description roughness of turned surfaces

Application of Clustering Filter for Noise and Outlier Suppression in Optical Measurement of Structured Surfaces

Contact Info

Product

Resources

About