Wear Model of Silicon Nitride Ceramic Balls in Three-Body Coupling Grinding Mode

Abstract

Silicon nitride ceramic balls are the key basic components of bearings in major equipment. Their key performance indices are accuracy and batch consistency. A grinding method with the most appropriate comprehensive performance is the basis and guarantee for optimizing these performance indices. In this study, an accurate wear model was established to predict the material removal rates (MRRs) of grinding methods and improve the dynamic grinding control ability of machinery during grinding, thus enabling the mass production of high-grade silicon nitride ceramic balls. A comprehensive analysis of various grinding and polishing methods revealed that the factors affecting sphericity were mainly manifested by the increase in ball sliding and the improvement in MRRs. More over, the three-body coupling grinding mode was considered as the grinding mode that was most applicable to silicon nitride ceramic balls. The upper disk served as an external nonlinear load, and the combination of the rotating speeds of the inner and outer disks of the lower grinding disk could actively control the ball’s angle of rotation. This three-body wear mode can fully envelop the grinding trajectory and ensure uniform grinding. The traditional two-body wear model was unsuitable for three-body coupling grinding. A wear model based on three-body wear was established to predict MRRs and understand the principle of material removal in the grinding of precision spheres. Theoretical analysis and experimental verification revealed that the MRRs of silicon nitride ceramic balls during wear are not only related to the process parameters of external load and speed but also to the physical properties and geometric parameters of balls, abrasives, and processing machinery. The wear model results of silicon nitride ceramic balls in the three-body coupling grinding mode can be obtained stably on the basis of the established wear model by removing adverse effects and adopting optimized processing parameters, thus verifying the correctness of the theoretical and simulation analyses.