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

Original Article


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 grinding Modelling Optimisation Genetic algorithms 



constant in mathematical model


constant in surface roughness model


constant in the number of flaws model


depth of cut, μm


degrees of freedom


table feed rate, mm/min


grit size (mesh)


material removal rate, mm3/mm width-min


number of flaws measured


surface roughness measured, μm


machining response


depth of cut exponent in mathematical model


depth of cut exponent in surface roughness model


depth of cut exponent in number of flaws model


feed rate exponent in mathematical model


feed rate exponent in surface roughness model


feed rate exponent in number of flaws model


grit size exponent in mathematical model


grit size exponent in surface roughness model


grit size exponent in number of flaws model


  1. 1.
    Inasaki I (1987) Grinding of hard and brittle materials. Annals CIRP 36(2):463–471Google Scholar
  2. 2.
    Malkin S, Hwang TW (1996) Grinding mechanisms for ceramics. Annals CIRP 45(2):569–580Google Scholar
  3. 3.
    Tonshoff HK, Peters J, Inasaki I, Paul T (1992) Modelling and simulation of grinding processes. Annals CIRP 41(2):677–688Google Scholar
  4. 4.
    Liao TW, Chen LJ (1994) A neural network approach for the grinding process: modelling and optimization. Int J Mach Tool and Manufact 34(7)919–937Google Scholar
  5. 5.
    Jain RK, Jain VK (2000) Optimum selection of machining conditions in abrasive flow using neural networks. J Mater Process Technol 108:62–67Google Scholar
  6. 6.
    Suresh PVS, Rao PV, Deshmukh SG (2002) A genetic algorithmic approach for optimization of surface roughness prediction model. Int J Mach Tools and Manufact 42:675–680Google Scholar
  7. 7.
    Konig W, Wemhoner J (1989) Optimizing grinding of SiC. Ceram Bullet 68(3):545–548Google Scholar
  8. 8.
    Mayer Jr JE, Fang GP (1995) Effect of grinding parameters on surface finish of ground ceramics. Annals CIRP 44(1):279–282Google Scholar
  9. 9.
    Montgomery DC (2001) Design and analysis of experiments. Wiley, SingaporeGoogle Scholar
  10. 10.
    Armarego EJA, Brown RH (1969) The machining of metals. Prentice-Hall, New JerseyGoogle Scholar
  11. 11.
    Goldberg DE (1999) Genetic algorithms. Addison-Wesley, New DelhiGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2003

Authors and Affiliations

  1. 1.Mechanical Engineering DepartmentIndian Institute of Technology DelhiNew DelhiIndia

Personalised recommendations