Optimization of Machining Parameters for Milling Operations Using Non-conventional Methods

  • N. BaskarEmail author
  • P. Asokan
  • G. Prabhaharan
  • R. Saravanan
Original Article


In this paper, optimization procedures based on the genetic algorithm, tabu search, ant colony algorithm and particle swarm optimization Algorithm were developed for the optimization of machining parameters for milling operation. This paper describes development and utilization of an optimization system, which determines optimum machining parameters for milling operations. An objective function based on maximum profit in milling operation has been used. An example has been presented at the end of the paper to give a clear picture from the application of the system and its efficiency. The results are compared and analysed using the method of feasible directions and handbook recommendations.


Ant colony algorithm Genetic algorithm  Optimization Multi-tool milling Particle swarm optimization algorithm Tabu search algorithm 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Brewer RC, Reuda RAA (1963) A simplified approach to the optimum selection of machining parameters. Eng Dig 24(9):131–151Google Scholar
  2. 2.
    Colding BN (1969) Machining economics and industrial data manuals. Ann CIRP 17:279–288Google Scholar
  3. 3.
    Ermer DS (1971) Optimization of the Constrained machining economics problem by geometric programming. Trans ASME J Eng Ind 93:1067–1072CrossRefGoogle Scholar
  4. 4.
    Lwata K, Murotsa Y, Jwotsubo T, Fuji S (1972) A probabilistic approach to the determination of the optimum cutting conditions. Trans ASME J Eng Ind 94:1099–1107CrossRefGoogle Scholar
  5. 5.
    Gopalakrishnan B, Faiz AK (1991) Machining parameter selection for turning with constraints: an analytical approach based on geometric programming. Int J Prod Res 29:1897–1908CrossRefGoogle Scholar
  6. 6.
    Rao SS, Hati SK (1978) Computerized Selection of Optimum Machining Conditions for a job Requiring multiple operations. Trans ASME J Eng Ind 100:356–362CrossRefGoogle Scholar
  7. 7.
    Shanmugham MS, Bhaskara Reddy SV, Narendran TT (2000) Selection of Optimal Conditions in Multi-Pass Face Milling using a genetic algorithm. Int J Mach Tool Manuf 40:401–414CrossRefGoogle Scholar
  8. 8.
    Baskar N, Asokan P, Saravanan R, Prabaharan G (2002) Selection of Optimal conditions in Multi-Pass Face Milling using Non Conventional Methods. Proceedings of the 20th All India Manufacturing Technology, Design and Research ConferenceGoogle Scholar
  9. 9.
    Ihsan Sonmez A et al. (1999) Dynamic optimization of multipass milling operations via genetic programming. Int J Mach Tool Manuf 39:297–320CrossRefGoogle Scholar
  10. 10.
    Zompi A, Levi R, Ravig Nani GL (1979) Multi-Tool Machining Analysis, Part I. Tool Feature Patterns Implications 101:230–236Google Scholar
  11. 11.
    Ravignani GL, Zompi A, Levi R (1979) Multi-Tool Machining Analysis, Part 2. Economic Evaluation in view of Tool life Scatter 101:237–240Google Scholar
  12. 12.
    Cakir MC, Gurarda A (2000) Optimization of machining conditions for multi-tool milling operations. Int J Prod Res 38:3537–3552CrossRefGoogle Scholar
  13. 13.
    Wang J, Armarego EJA (1995) Optimization Strategies and CAM software for multiple constraint face milling operations. 6th Int. Conference on Manufacturing Engineering (ICME’95), 29 Nov–1 Dec; Melbourne, Australia, pp 535–540Google Scholar
  14. 14.
    Tolouei-Rad M (1997) On the optimization of machining parameters for milling operations. Int J Mach Tool Manuf 37(1):1–16CrossRefGoogle Scholar
  15. 15.
    Baskar N, Asokan P, Saravanan R, Prabaharan G (2003) Optimization of machining parameters for Milling operations using Particle Swarm Optimization algorithm. Proc MOSIM – 2003, D13–21Google Scholar
  16. 16.
    Saravanan R (2001) Optimization of operating parameters for CNC manufacturing systems using conventional and non-conventional techniques (GA). PhD thesis, Regional Engineering College, Bharathidasan University, TiruchirappalliGoogle Scholar
  17. 17.
    Jayaram VK et al. (2000) Ant colony frame work for optimal design and scheduling of batch plants. Int J Comput Chem Eng 24:1901–1912CrossRefGoogle Scholar
  18. 18.
    Dorigo M et al. (1996) The Ant system: Optimization by a colony of cooperating agents. IEEE Trans Syst, Man and cybernetics-part B 26(1):1–13Google Scholar
  19. 19.
    Dorigo M et al. (1997) Ant Colony System: A Cooperative learning approach to the traveling Sales man Problem. IEEE Trans Evol Comput 1:53–66CrossRefGoogle Scholar
  20. 20.
    Glover F (1990) Tabu Search. Part II ORSA J Comput 2:4–32CrossRefGoogle Scholar
  21. 21.
    Kennedy J et al. (1995) Particle swarm optimization. Proc. IEEE Int’l Conf on Neural Networks, IV, 1942–1948Google Scholar

Copyright information

© Springer-Verlag 2004

Authors and Affiliations

  • N. Baskar
    • 1
    Email author
  • P. Asokan
    • 2
  • G. Prabhaharan
    • 2
  • R. Saravanan
    • 3
  1. 1.School of Mechanical EngineeringShanmugha Arts, Science, Technology & Research Academy, (SASTRA, Deemed University) ThanjavurTamilnaduIndia
  2. 2.Department of Production EngineeringRegional Engineering CollegeTiruchirappalliIndia
  3. 3.Department of Mechanical EngineeringJ.J. College of Engineering & TechnologyTiruchirappalliIndia

Personalised recommendations