Modeling of the Grinding Wheel Topography Depending on the Recipe-Dependent Volumetric Composition

Abstract

The prediction of the grinding process result, such as the workpiece surface quality or the state of the edge zone depending on the used grinding wheel is still a great challenge for today's manufacturers and users of grinding tools. This is mainly caused by an inadequate predictability of force and temperature affecting the process. The force and the temperature strongly depend on the topography of the grinding wheel, which comes into contact with the workpiece during the grinding process. The topography of a grinding wheel mainly depends on the structure of the grinding wheel, which is determined by the recipe-dependent volumetric composition of the tool. So, the structure of a grinding tool determines its application behavior strongly. As result, the knowledge-based prediction of the grinding wheel topography and its influence on the machining behavior will only be possible if the recipe-dependent grinding wheel structure is known. This paper presents an innovative approach for modeling the grinding wheel structure and the resultant grinding wheel topography. The overall objective of the underlying research work was to create a mathematical-generic grinding tool model in which the spatial arrangement of the components, grains, bond, and pores, is simulated in a realistic manner starting from the recipe-dependent volumetric composition of a grinding wheel. This model enables the user to determine the resulting grinding wheel structure and the grinding wheel topography of vitrified and synthetic resin-bonded cubic boron nitride (CBN) grinding wheels depending on their specification and thus to predict their application behavior. The originality of the present research results is a generic approach for the modeling of grinding tools, which takes into account the entire grinding wheel structure to build up the topography. Therefore, original mathematical methods are used. The components of grinding wheels are analyzed, and distribution functions of the component's positions in the tools are determined. Thus, the statistical character of the grinding wheel structure is taken into account in the developed model. In future, the presented model opens new perspectives in order to optimize and to increase the productivity of grinding processes.