The International Journal of Advanced Manufacturing Technology

, Volume 22, Issue 7, pp 475–480

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

Authors

  • Anne Venu Gopal
    • Mechanical Engineering DepartmentIndian Institute of Technology Delhi
    • Mechanical Engineering DepartmentIndian Institute of Technology Delhi
Original Article

DOI: 10.1007/s00170-002-1494-9

Cite this article as:
Gopal, A.V. & Rao, P.V. Int J Adv Manuf Technol (2003) 22: 475. doi:10.1007/s00170-002-1494-9

Abstract

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.

Key words

Ceramic grindingModellingOptimisationGenetic algorithms

Nomenclature

C

constant in mathematical model

C1

constant in surface roughness model

C2

constant in the number of flaws model

d

depth of cut, μm

dof

degrees of freedom

f

table feed rate, mm/min

M

grit size (mesh)

MRR

material removal rate, mm3/mm width-min

Nc

number of flaws measured

Ra

surface roughness measured, μm

Y

machining response

α

depth of cut exponent in mathematical model

α1

depth of cut exponent in surface roughness model

α2

depth of cut exponent in number of flaws model

β

feed rate exponent in mathematical model

β1

feed rate exponent in surface roughness model

β2

feed rate exponent in number of flaws model

γ

grit size exponent in mathematical model

γ1

grit size exponent in surface roughness model

γ2

grit size exponent in number of flaws model

Copyright information

© Springer-Verlag London Limited 2003