Surface topography quantification of super hard abrasive tools by laser scanning microscopy
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Non-conventional super hard abrasive tools are made of composite materials containing super hard grains, e.g., diamond or cubic boron nitride (CBN) grains, bound by a metallic constitutive phase. These tools are usually produced by means of sintering, and are widely applied in the abrasive machining processes of modern manufacturing, especially in precision machining. The abrasive grains, which induce the material removal processes, are embedded in the metallic binder. They emerge as a consequence of self-dressing, resulting in a self-sharping effect. Therefore, the cutting surface of the tool displays an irregular topography. Quantification of surface topography scenario may supply valuable information to evaluate and understand its correlation to wear mechanisms. In this study, an experimental protocol consisting of five steps: specimen preparation, surface scanning, image assembly, image digital processing and surface quantification, was proposed and validated by characterizing two CBN honing tools used for precision machining: B151/L2/2010/50 (B151) and B91/128/x44/35 (B91) CBN honing stones. It involved the use of laser scanning microscopy and digital imaging processing for assessing significant dimensional, geometrical, and positional properties of CBN grains at the surface of super hard abrasive tools. It was shown that surface topography quantification is an effective method to evaluate and obtain the defined parameters. However, smaller grains may require images with higher resolution; thus, scanning must be refined. Finally, a critical comparative analysis of the experimental results attained for the studied tools pointed out honing stone B91 as more appropriated than B151 one for achieving a higher machining quality of the workpiece.
CitationFang, S., Llanes, L., Engstler, M., Baehre, D., Soldera, F., Muecklich, F. Surface topography quantification of super hard abrasive tools by laser scanning microscopy. "Materials Performance and Characterization", 2016, vol. 5, núm. 5, p. 796-815.