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Multi-objective optimization of multi-pass face milling using particle swarm intelligence

  • Wen-an Yang
  • Yu Guo
  • Wenhe Liao
ORIGINAL ARTICLE

Abstract

In this paper, to facilitate manufacturing engineers have more control on the machining operations, the optimization issue of machining parameters is handled as a multi-objective optimization problem. The optimization strategy is to simultaneously minimize production time and cost and maximize profit rate meanwhile subject to satisfying the constraints on the machine power, cutting force, machining speed, feed rate, and surface roughness. An efficient fuzzy global and personal best-mechanism-based multi-objective particle swarm optimization (F-MOPSO) to optimize the machining parameters is developed to solve such a multi-objective optimization problem in optimization of multi-pass face milling. The proposed F-MOPSO algorithm is first tested on several benchmark problems taken from the literature and evaluated with standard performance metrics. It is found that the F-MOPSO does not have any difficulty in achieving well-spread Pareto optimal solutions with good convergence to true Pareto optimal front for multi-objective optimization problems. Upon achieving good results for test cases, the algorithm was employed to a case study of multi-pass face milling. Significant improvement is indeed obtained in comparison to the results reported in the literatures. The proposed scheme may be effectively employed to the optimization of machining parameters for multi-pass face milling operations to obtain efficient solutions.

Keywords

Face milling Multi-pass Machining parameters Multi-object optimization Particle swarm optimization 

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References

  1. 1.
    Iwata K, Murotsu Y, Iwotsubo T, Fuji S (1972) A probabilistic approach to the determination of the optimum cutting conditions. Trans ASME J Eng Ind 94:1099–1107CrossRefGoogle Scholar
  2. 2.
    Gopalakrishnan B, Faiz AK (1991) Machining parameter selection for turning with constraints: an analytical approach based on geometric programming. Int J Prod Res 29:1897–1908zbMATHCrossRefGoogle Scholar
  3. 3.
    Tolouei-Rad M, Bidhendi IM (1997) On the optimization of machining parameters for milling operations. Int J Mach Tools Manuf 37(1):1–16CrossRefGoogle Scholar
  4. 4.
    Sonmez AI, Baykasoglu A, Dereli T, Filiz IH (1999) Dynamic optimization of multipass milling operationsvia geometric programming. Int J Mach Tools Manuf 39(2):297–320CrossRefGoogle Scholar
  5. 5.
    Jha NK (1990) A discrete data base multiple objective optimization of milling operation through geometric programming. Trans ASME J Eng Ind 112(4):368–374CrossRefGoogle Scholar
  6. 6.
    Wang J (1993) Constrained optimization of rough peripheral and end milling operations. PhD Thesis, University of Melbourne, AustraliaGoogle Scholar
  7. 7.
    Petropoulos PG (1973) Optimal selection of machining rate variable by geometric programming. Int J Prod Res 11(4):305–314CrossRefGoogle Scholar
  8. 8.
    Shunmugam MS, Bhaskara Reddy SV, Narendran TT (2000) Selection of optimal conditions in multi-pass face-milling using a genetic algorithm. Int J Mach Tools Manuf 40:401–414CrossRefGoogle Scholar
  9. 9.
    An LB, Chen MY (2003) On optimization of machining parameters. in: Proceedings of the 4th International Conference on Control and Automation. pp 839-843Google Scholar
  10. 10.
    Wang ZG, Rahman M, Wong YS, Sun J (2004) Optimisation of multi-pass milling using genetic algorithm and genetic simulated annealing. Int J Adv Manuf Technol 24:727–732CrossRefGoogle Scholar
  11. 11.
    Saha SK (2009) Genetic algorithm based optimization and post optimality analysis of multi-pass face milling. CoRR abs/0902.0763Google Scholar
  12. 12.
    Conceição António CA, Castro CF, Davim JP (2009) Optimisation of multi-pass cutting parameters in face-milling based on genetic search. Int J Adv Manuf Technol 44:1106–1115CrossRefGoogle Scholar
  13. 13.
    Deb K (1999) Multi-objective evolutionary algorithms: introducing bias among Pareto-optimal solutions. Technical Report KanGAL Report No. 99002. Indian Institute of Technology, IndiaGoogle Scholar
  14. 14.
    Kennedy J, Eberhart RC (1995) Particle swarm optimization. in: Proceedings of the IEEE International Conference on Neural Networks, 27 November/December, Perth, Australia, IV. Piscataway, NJ: IEEE Service Centre, pp 1942–1948Google Scholar
  15. 15.
    Natarajan U, Periasamy VM, Saravanan R (2007) Application of particle swarm optimisation in artificial neural network for the prediction of tool life. Int J Adv Manuf Technol 31:871–876CrossRefGoogle Scholar
  16. 16.
    Gandhi AK, Kumar SK, Pandey MK, Tiwari MK (2009) EMPSO-based optimization for inter-temporal multi-product revenue management under salvage consideration. Appl Soft Comput J. doi: 10.1016/j.asoc.2009.12.006 Google Scholar
  17. 17.
    Yang ZG, Zhou YS, Wu M (2005) Application of improved particle swarm optimization in economic dispatching. Power Syst Technol 29(2):1–4Google Scholar
  18. 18.
    Jerald J, Asokan P, Prabaharan G, Saravanan R (2005) Scheduling optimization of flexible manufacturing systems using particle swarm optimization algorithm. Int J Adv Manuf Technol 25:964–971CrossRefGoogle Scholar
  19. 19.
    Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197CrossRefGoogle Scholar
  20. 20.
    Nefedov N, Osipov K (1987) Typical examples and problems in metal cutting and tool design. Mir Publishers, MoscowGoogle Scholar
  21. 21.
    Dubois D, Prade H (1998) Possibility theory: an approach to computerized processing and uncertainty. Plenum Press, New YorkGoogle Scholar
  22. 22.
    Lovbjerg M, Rasmussen TK, Krink T (2001) Hybrid particle swarm optimiser with breeding and subpopulations. in: Proceedings of the 3rd Genetic and Evolutionary Computation Conference, San Francisco, CA.Google Scholar
  23. 23.
    Higashi N, Iba H (2003) Particle swarm optimization with Gaussian mutation. in: Proceedings of the IEEE Swarm Intelligence Symposium, Indianapolis: IEEE Inc, pp 72–79Google Scholar
  24. 24.
    Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans Evol Comput 3:257–271CrossRefGoogle Scholar
  25. 25.
    Knowles JD, Corne DW (2000) Approximating the nondominated front using the Pareto archived evolution strategy. Evol Comput 8:149–172CrossRefGoogle Scholar
  26. 26.
    Zitzler E, Laumanns M, Thiele L (2001) SPEA2: Improving the strength Pareto evolutionary algorithm. in: Proceedings the EUROGEN 2001—Evolutionary Methods for Design, Optimisation and Control with Applications to Industrial Problems, pp 95-100Google Scholar
  27. 27.
    Kursawe F (1991) A variant of evolution strategies for vector optimization. in: Proceedings of the 1st Workshop on Parallel Problem Solving from Nature, Springer-Verlag London, UK, pp 193–197Google Scholar
  28. 28.
    Kita H, Yabumoto Y, Mori N, Nishikawa Y (1996) Multi-objective optimization by means of the thermodynamical genetic algorithm. in: Proceedings the 4th International Conference on Parallel Problem Solving from Nature, Springer-Verlag London, UK, pp 504–512Google Scholar
  29. 29.
    Deb K (1999) Multi-objective genetic algorithms: problem difficulties and construction of test problems. Evol Comput 7:205–230CrossRefGoogle Scholar
  30. 30.
    Lee TS, Ting TO, Lin YJ, Htay T (2007) A particle swarm approach for grinding process optimization analysis. Int J Adv Manuf Technol 33:1128–1135CrossRefGoogle Scholar
  31. 31.
    Srinivas J, Giri R, Yang SH (2009) Optimization of multi-pass turning using particle swarm intelligence. Int J Adv Manuf Technol 40:56–66CrossRefGoogle Scholar
  32. 32.
    Jain NK, Jain VK (2007) Optimization of electrochemical machining process parameters using genetic algorithm. Machining Sci Technol 11:235–258CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Department of Manufacturing Engineering of Aeronautics and AstronauticsNanjing University of Aeronautics and AstronauticsNanjingPeople’s Republic of China

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