Improvement in productivity in SiC grinding

Abstract

The selection of optimum machining conditions, during ceramic grinding, is of great concern in manufacturing industries these days. The increasing quality demands, at higher productivity levels, require the ceramic grinding process to be executed more efficiently. Specifically, the material removal rate needs to be maximized while controlling the surface quality. Despite extensive research on the ceramic grinding process, determining the desirable operating conditions in industrial setting still relies on the skill of the operators and trial-and-error methods. In the present work, an attempt has been made to optimize the grinding conditions for maximum material removal rate using a multi-objective function model, with surface roughness and tangential grinding force as user definable constraints. Experiments were carried out to study the effect of various parameters namely the depth of cut, table feed, grit size, and grit density on the surface roughness and tangential grinding force. Mathematical models were developed using the experimental data considering only the significant parameters. In order to optimize the grinding conditions for maximum material removal rate, a genetic algorithm (GA) code was developed. The manufacturer’s constraints, based on the functional requirements of the components for maximum production rate, have been included in the GA code.