Surface roughness modeling for grinding of Silicon Carbide ceramics considering co-existence of brittleness and ductility

Ground surface roughness is always hard to be predicted due to the random distribution of abrasive grits and various materials properties, especially for brittle materials. In grinding of brittle materials, because of the brittle and hard nature, the material will be removed in both ductile and brittle modes, which make the modeling of surface roughness difficult. Therefore, this paper is intended to propose a new surface roughness model for brittle materials by considering the co-existing of machining-induced ductility and brittleness. A Rayleigh distribution function will be used to model the randomness of abrasive grits by assuming that the chip thickness for a single grit conforms to this distribution. In the meanwhile, the critical chip thickness model, considering both the materials properties and process parameters, will be adopted to divide the ductile and brittle grinding for a single grit. Thus, the probability for ductile mode grinding can be obtained through integral calculation of the Rayleigh distribution function. The grinding damage model from the indentation fracture mechanics is used to model the brittle surface roughness. The surface roughness and chip thickness model was calibrated and validated through a series of experiments on grinding of Silicon Carbide. The results show that the model predictions have a good match with the experimental results and the ductile surface roughness is much smaller than the brittle mode. Therefore, it can be concluded that the surface roughness modeling of brittle materials could benefit from considering the co-existence of brittleness and ductility governed by the machining kinematics, abrasive topology and process parameters, as well as the grinding induced surface damages. Moreover, the ductility-dominant grinding, under a higher wheel speed or lower chip thickness, can help to improve the surface roughness with a lower level of damage. (C) 2017 Published by Elsevier Ltd.