Optimization of multi-pass face milling using a fuzzy particle swarm optimization algorithm

  • Wen-an Yang
  • Yu Guo
  • Wen-he Liao


In this paper, a simple methodology to distribute the total stock removal in each of the rough passes and the final finish pass and a fuzzy particle swarm optimization (FPSO) algorithm based on fuzzy velocity updating strategy to optimize the machining parameters are proposed and implemented for multi-pass face milling. The optimum value of machining parameters including number of passes, depth of cut in each pass, speed, and feed is obtained to achieve minimum production cost while considering technological constraints such as allowable machine power, machining force, machining speed, tool life, feed rate, and surface roughness. The proposed FPSO algorithm is first tested on few benchmark problems taken from the literature. Upon achieving good results for test cases, the algorithm was employed to two illustrative case studies of multi-pass face milling. Significant improvement is also obtained in comparison to the results reported in the literatures, which reveals that the proposed methodology for distribution of the total stock removal in each of passes is effective, and the proposed FPSO algorithm does not have any difficulty in converging towards the true optimum. From the given results, the proposed schemes may be a promising tool for the optimization of machining parameters.


Face milling Multi-pass Machining parameters Optimization Particle swarm optimization 


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This work was supported by the National Defense Foundation of China under grant D2520062. The authors would like to express their sincere appreciation to the referees for their detailed and helpful comments to improve the quality of the paper.

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  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 29:1897–1908CrossRefzbMATHGoogle 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 operations via 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.
    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 1942–1948.Google Scholar
  14. 14.
    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
  15. 15.
    Gandhi AK, Kumar SK, Pandey MK, Tiwari MK (2010) 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
  16. 16.
    Yang ZG, Zhou YS, Wu M (2005) Application of improved particle swarm optimization in economic dispatching. Power Syst Technol 29(2):1–4Google Scholar
  17. 17.
    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
  18. 18.
    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
  19. 19.
    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
  20. 20.
    Higashi N, Iba H (2003) Particle swarm optimization with Gaussian mutation. In: Proceedings of the IEEE Swarm Intelligence Symposium 2003 (SIS 2003), Indianapolis, Indiana, USA, pp 72–79Google Scholar
  21. 21.
    Nefedov N, Osipov K (1987) Typical examples and problems in metal cutting and tool design. Mir Publishers, MoscowGoogle Scholar
  22. 22.
    Jain NK, Jain VK (2007) Optimization of electrochemical machining process parameters using genetic algorithm. Mach Sci Technol 11:235–258CrossRefGoogle Scholar
  23. 23.
    Coello CA (2000) Use of self-adaptive penalty approach for engineering optimization problems. Comput Ind 41(2):113–127CrossRefGoogle Scholar
  24. 24.
    Deb K (1997) GeneAS: a robust optimal design technique for mechanical component design. In: Dipankar Dasgupta and Zbigniew Michalewicz (eds) Evolutionary algorithms in engineering applications, Springer, pp 497–514Google Scholar
  25. 25.
    Deb K (1991) Optimization design of a welded beam via genetic algorithm. AIAA J 29(11):2013–2015CrossRefGoogle Scholar
  26. 26.
    Arora JS (1989) Introduction to Optimum Design. McGraw-Hill.Google Scholar
  27. 27.
    Hu XH, Eberhart RC, Shi YH (2003) Engineering optimization with particle swarm. In: Proceedings of the IEEE Swarm Intelligence Symposium 2003 (SIS 2003), Indianapolis, Indiana, USA, pp 53–57Google Scholar

Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  1. 1.School of Mechanical and Electrical EngineeringNanjing University of Aeronautics and AstronauticsNanjingPeople’s Republic of China

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