A probabilistic approach to predict surface roughness in ceramic grinding

The quality of the surface produced during ceramic grinding is important as it influences the performance of the finished part to great extent. Hence, the estimation of surface roughness can cater to the requirements of performance evaluation. But, the surface finish is governed by many factors and its experimental determination is laborious and time consuming. So the establishment of a model for the reliable prediction of surface roughness is still a key issue for ceramic grinding. In this study, a new analytical surface roughness model is developed on the basis of stochastic nature of the grinding process, governed mainly by the random geometry and the random distribution of cutting edges. This model has been validated by the experimental results of silicon carbide grinding. The theoretical analysis yielded values which agree reasonably well with the experimental results.