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[103] utilized a laser line scanning system to assess surface texture and skid resistance.
The extraction of protruding diamond grains feature from the measured data of grinding wheels is the key to assessing their surface texture and predicting their functional performance.
Consequently, this study aims at setting up and validating simple methods for assessing surface texture and skid resistance based on core/sample measurements.
Next, by adding surface texture, k-means clustering was performed globally and the polygons were grouped into 40 clusters.
The gum surface texture (stippling) was assessed visually and evaluated qualitatively.
The crystalline structure and surface texture of the samples are assessed by means of optical, electron and confocal microscopy.
We classified the base polygons created from the 280 m DEM using normalized LN slope, local convexity and surface texture by k-means clustering.
However, with these methods the surface texture can only be assessed before applying the concrete overlay.
Results showed that significant changes in mean dragging force occurred with changes in both surface texture and slope.
Mean values for each geometric signature (LN slope, surface texture (3 × 3 and 13 × 13), combined texture, and local convexity) within the destination polygons were calculated and stored as representative values.
Evaluation of the zirconia implant surface roughness as well as their surface texture parameters is carried out by means of confocal laser scanning microscopic technique.
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