The optimisation of the grinding of silicon carbide with diamond wheels using genetic algorithms

Modelling and optimisation are necessary for the control of any process to achieve improved product quality, high productivity and low cost. The grinding of silicon carbide is difficult because of its low fracture toughness, making it very sensitive to cracking. The efficient grinding of high performance ceramics involves the selection of operating parameters to maximise the MRR while maintaining the required surface finish and limiting surface damage. In the present work, experimental studies have been carried out to obtain optimum conditions for silicon carbide grinding. The effect of wheel grit size and grinding parameters such as wheel depth of cut and work feed rate on the surface roughness and damage are investigated. The significance of these parameters, on the surface roughness and the number of flaws, has been established using the analysis of variance. Mathematical models have also been developed for estimating the surface roughness and the number of flaws on the basis of experimental results. The optimisation of silicon carbide grinding has been carried out using genetic algorithms to obtain a maximum MRR with reference to surface finish and damage.Nomenclature C constant in mathematical model - C₁ constant in surface roughness model - C₂ constant in the number of flaws model - d depth of cut, m - dof degrees of freedom - f table feed rate, mm/min - M grit size (mesh) - MRR material removal rate, mm³/mm width-min - N_c number of flaws measured - R_a surface roughness measured, m - Y machining response - depth of cut exponent in mathematical model - ₁ depth of cut exponent in surface roughness model - ₂ depth of cut exponent in number of flaws model - feed rate exponent in mathematical model - ₁ feed rate exponent in surface roughness model - ₂ feed rate exponent in number of flaws model - grit size exponent in mathematical model - ₁ grit size exponent in surface roughness model - ₂ grit size exponent in number of flaws model