The grain geometrical features of grinding wheels are critically important to evaluate their quality and grinding performance. Therefore, grain geometrical features should be comprehensively and systematically characterized. Such characterization parameters were defined from the geometric features of individual grains (for each grain) to the distribution features of grains (for a group of grains on a given wheel surface) on the basis of grain cross-sections. For each grain, characterization parameters included cross-section area, equivalent diameter, volume, and sharpness ratio of the grain. For a group of grains, the characterization parameters were distribution density, cross-section area, cross-section area-supporting rate, and volume proportion of the grains on a sectioning depth. The cross-sections of grains were extracted from the section-scanning images captured by an optical measurement system. A system based on optical vertical scanning using a narrow depth of field microscope was adopted to obtain the optical sectioning images, and the grain cross-section boundaries on each image were extracted through an image-processing program. The defined characterization parameters, vertical sectioning step, and sectioning numbers were calculated efficiently. A monolayer brazed grinding wheel with a grain size of 425–500 μm was selected for an experiment. The individual and group parameters, such as sharpness or strength, active volume, or area-supporting ratio, of grains were used for performance analysis. Experimental results indicated that the proposed parameters based on grain cross-sections are not only efficient to calculate but also effective to characterize the geometric features of grains for monolayer brazed grinding wheels.
Characterization Grain geometric features Grain distribution Monolayer brazed grinding wheel Cross-section Optical system Narrow depth of field
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This research is funded by the National Natural Science Foundation of China (Grant Nos. U1805251, 51235004), and Xiamen Science and Technology Project (Grant No. 3502Z20183019).
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