Skip to main content

Advertisement

SpringerLink
Log in

Ants colony algorithm approach for multi-objective optimisation of surface grinding operations

  • Original Article
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

An ant colony based optimisation procedure has been developed to optimise grinding conditions, viz. wheel speed, workpiece speed, depth of dressing and lead of dressing, using a multi-objective function model with a weighted approach for the surface grinding process. The procedure evaluates the production cost and production rate for the optimum grinding condition, subjected to constraints such as thermal damage, wheel wear parameters, machine tool stiffness and surface finish. The results are compared with Genetic Algorithm (GA) and Quadratic Programming (QP) techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Abbreviations

a p :

down feed of grinding (mm/pass)

a w :

total thickness of cut (mm)

A o :

initial wear flat-area percentage (%)

b e :

empty width of grinding (mm)

b s :

width of wheel (mm)

b w :

width of workpiece (mm)

B k :

positive definite approximation of the Hessian

doc :

depth of dressing (mm)

c d :

cost of dressing ($)

c s :

cost of wheel per mm3 ($/mm3)

CT :

total production cost ($/pc)

CT * :

expected production cost limit ($/pc)

d g :

grind size (mm)

D e :

diameter of wheel (mm)

f b :

cross feed rate (mm/pass)

G :

grinding ratio

k a :

constant dependent on coolant and wheel grind type

k u :

wear constant (mm-1)

k c :

cutting stiffness (N/mm)

k m :

static machine stiffness (N/mm)

k s :

wheel wear stiffness (N/mm)

L :

lead of dressing (mm/rev)

L e :

empty length of grinding (mm)

L w :

length of workpiece (mm)

M c :

cost per hour labour and administration ($/h)

N d :

total number of pieces to be grouped during the life of dressing (pc)

N t :

batch size of workpieces (pc)

N td :

total number of workpieces to be grouped during the life of dressing (pc)

P :

number of workpieces loaded on the table (pc)

R a :

surface roughness (µm)

R a*:

surface finish limit during rough grinding (µm)

R c :

workpiece hardness (Rockwell hardness number)

R em :

dynamic machine characteristics

S d :

distance of wheel idling (mm)

S p :

number of spark out grinding (pass)

t sh :

time of adjusting machine tool (min)

t i :

time of loading and unloading workpiece (min)

T ave :

average chip thickness during grinding (µm)

U :

specific grinding energy (J/mm)

U * :

critical specific grinding energy (J/mm3)

V r :

speed of wheel idling (mm/min)

V s :

wheel speed (m/min)

V w :

workpiece speed (m/min)

VOL :

wheel bond percentage (%)

WRP :

workpiece removal parameter (mm3/min-N)

WRP * :

workpiece removal parameter limit (mm3/min-N)

WWP :

wheel wear parameter (mm3/min-N)

W i :

weighting factor, 0≤W i≤1 (W 1+W 2+W 3=1)

References

  1. Malkin S (1985) Practical approaches to grinding optimization. Milton C. Shaw Grinding Symposium, ASME Winter Annual Meeting, Florida, USA, pp 289–299

  2. Field M et al. (1978) Optimizing grinding parameters to combine high productivity with high surface integrity. Ann CIRP 2791:523–536

    Google Scholar 

  3. Amitay G (1981) Adaptive control optimization of grinding. J Eng Ind pp 103–108

  4. Wen XM, Tay AAO, Nee A-YC (1992) Microcomputer based optimization of the surface grinding process. J Mater Process Tech 29:75–90

    Google Scholar 

  5. Saravanan R, Vengadesan S, Sachithanandam M (1998) Selection of operating parameters in surface grinding process using genetic algorithm (GA). Proceedings of the 18th All India Manufacturing Technology Design and Research Conference, pp 167–171

  6. Field et al (1980) Computerized cost analysis of grinding parameters. Ann CIRP 29(1):233–237

    Google Scholar 

  7. Saravanan R et al (2002) A multi objective genetic algorithm approach for optimization of surface grinding operations. Int J Mach Tool Manu 42:1327–1334

    Article  Google Scholar 

  8. Malkin S (1976) Selection of operating parameters in surface grinding of steels. J Eng Ind 98:56–62

    Google Scholar 

  9. Malkin S (1974) Thermal aspects of grinding, part 2, surface temperature and workpiece burn. J Eng Ind 96:1184–1191

    Google Scholar 

  10. King RI, Hahn S (1986) Handbook of modern grinding technology. Chapman and Hall, London

  11. Malkin S (1989) Grinding technology. Ellis Harwood, Chichester, UK

  12. Thompson RA (1971) The dynamic behavior of surface grinding. J Eng Ind 93:485–497

    Google Scholar 

  13. Jayaram VK, Kulkarni BD, Karale S, Shelokar P (2000) Ant colony frame work for optimal design and scheduling of batch plants. Int J Comput Chem Eng 24:1901–1912

    Article  Google Scholar 

  14. Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE T Syst Man Cyb B 26(1):1–13

    Article  Google Scholar 

  15. Maniezzo V, Colorni A (1999) The ant system applied to the quadratic assignment problem. IEEE T Knowl Data En 11(5):769–778

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Sastra University, Tanjore, India

    N. Baskar

  2. Department of Mechanical Engineering, JJ College of Engineering, Trichy, India

    R. Saravanan

  3. Department of Production Engineering, Regional Engineering College, Trichirapalli, India

    P. Asokan & G. Prabhaharan

Authors
  1. N. Baskar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Saravanan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. Asokan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. G. Prabhaharan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to N. Baskar.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Baskar, N., Saravanan, R., Asokan, P. et al. Ants colony algorithm approach for multi-objective optimisation of surface grinding operations. Int J Adv Manuf Technol 23, 311–317 (2004). https://doi.org/10.1007/s00170-002-1533-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-002-1533-6

Keywords

Search

Navigation