Optimization of machining economics and energy consumption in face milling operations

  • Yi-Chi Wang
  • Dong-Won Kim
  • Hiroshi Katayama
  • Wen-Chin Hsueh
ORIGINAL ARTICLE
  • 31 Downloads

Abstract

Metal cutting (or machining) is one important aspect of the manufacturing system. Selecting optimal cutting conditions for machining is then a crucial process planning task for manufacturing. Traditionally, solving such machining problems was only focused on economic objectives such as maximizing profit or minimizing production time requirement. In the recent decade, however, minimizing energy consumption in manufacturing processes has attracted increased attention due to increasing energy costs and concern with greenhouse gas emissions. Energy loss could be avoided by carefully selecting cutting parameters. This paper develops a multi-objective mathematical model to minimize unit production costs along with energy consumption for face milling operations. In addition, an evolutionary strategy (ES)-based optimization approach is used to identify optimal cutting conditions for the proposed model.

Keywords

Machining economics Energy consumption Face milling Evolutionary strategy 

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References

  1. 1.
    Gutowski T, Murphy C, Allen D, Bauer D, Bras B, Piwonka T, Sheng P, Sutherland J, Thurston D, Wolff E (2005) Environmentally benign manufacturing: observations from Japan, Europe, and the United States. J Clean Prod 13(1):1–17.  https://doi.org/10.1016/j.jclepro.2003.10.004 CrossRefGoogle Scholar
  2. 2.
    Bunse K, Vodicka M, Schönsleben P, Brülhart M, Ernst FO (2011) Integrating energy efficiency performance in production management—gap analysis between industrial needs and scientific literature. J Clean Prod 19(6–7):667–679.  https://doi.org/10.1016/j.jclepro.2010.11.011 CrossRefGoogle Scholar
  3. 3.
    Gutowski T, Dahmus J, Thirez A (2006) Electrical energy requirements for manufacturing processes. The 13th CIRP International Conference of Life Cycle Engineering 31:623–638 Lueven, May31–June 2Google Scholar
  4. 4.
    Mukherjee I, Ray PK (2006) A review of optimization techniques in metal cutting processes. Comput Ind Eng 50(1–2):15–34.  https://doi.org/10.1016/j.cie.2005.10.001 CrossRefGoogle Scholar
  5. 5.
    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(3):401–414.  https://doi.org/10.1016/S0890-6955(99)00063-2 CrossRefGoogle Scholar
  6. 6.
    An L, Chen M (2003) On optimization of machining parameters. The 4th International Conference on Control and Automation, pp.839–843, Montreal, Canada, June 10–12, 2003. doi:  https://doi.org/10.1109/ICCA.2003.1595141
  7. 7.
    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 44(11):1106–1115.  https://doi.org/10.1007/s00170-009-1933-y CrossRefGoogle Scholar
  8. 8.
    Zarei O, Fesanghary M, Farshi B, Jalili Saffar R, Razfar MR (2009) Optimization of multi-pass face-milling via harmony search algorithm. J Mater Process Technol 209(5):2386–2392.  https://doi.org/10.1016/j.jmatprotec.2008.05.029 CrossRefGoogle Scholar
  9. 9.
    Yang WA, Guo Y, Liao W (2011) Multi-objective optimization of multi-pass face milling using particle swarm intelligence. Int J Adv Manuf Technol 56(5):429–443.  https://doi.org/10.1007/s00170-011-3187-8 CrossRefGoogle Scholar
  10. 10.
    Zein A (2012) Transition towards energy efficiency machine tools. Springer Science & Business MediaGoogle Scholar
  11. 11.
    Diaz N, Redelsheimer E, Dornfeld D (2011) Energy consumption characterization and reduction strategies for milling machine tool use. Glocalized solutions for sustainability in manufacturing. Springer, Berlin Heidelberg, pp 263–267.  https://doi.org/10.1007/978-3-642-19692-8_46 Google Scholar
  12. 12.
    Mori M, Fujishima M, Inamasu Y, Oda Y (2011) A study on energy efficiency improvement for machine tools. CIRP Ann Manuf Technol 60(1):145–148.  https://doi.org/10.1016/j.cirp.2011.03.099 CrossRefGoogle Scholar
  13. 13.
    Kianinejad K, Uhlmann E, Peukert B (2015) Investigation into energy efficiency of outdated cutting machine tools and identification of improvement potentials to promote sustainability. Procedia CIRP 26:533–538.  https://doi.org/10.1016/j.procir.2014.07.083 CrossRefGoogle Scholar
  14. 14.
    Li W, Kara S (2011) An empirical model for predicting energy consumption of manufacturing processes: a case of turning process. Proc IMechE, Part B: J Eng Manuf 225(9):1636–1646CrossRefGoogle Scholar
  15. 15.
    Kara S, Li W (2011) Unit process energy consumption models for material removal processes. CIRP Ann Manuf Technol 60(1):37–40.  https://doi.org/10.1016/j.cirp.2011.03.018 CrossRefGoogle Scholar
  16. 16.
    Li W, Winter M, Kara S, Herrmann C (2012) Eco-efficiency of manufacturing processes: a grinding case. CIRP Ann Manuf Technol 61(1):59–62.  https://doi.org/10.1016/j.cirp.2012.03.029 CrossRefGoogle Scholar
  17. 17.
    Li L, Yan J, Xing Z (2013) Energy requirements evaluation of milling machines based on thermal equilibrium and empirical modeling. J Clean Prod 52:113–121.  https://doi.org/10.1016/j.jclepro.2013.02.039 CrossRefGoogle Scholar
  18. 18.
    Yoon HS, Kim ES, Kim MS, Lee JY, Lee GB, Ahn SH (2015) Towards greener machine tools—a review on energy saving strategies and technologies. Renew Sust Energ Rev 48:870–891.  https://doi.org/10.1016/j.rser.2015.03.100 CrossRefGoogle Scholar
  19. 19.
    Isakov E (2003) Engineering formulas for metalcutting, Industrial Press, Inc.Google Scholar
  20. 20.
    Bäck T (1996) Evolutionary algorithms in theory and practice. Oxford University Press, New YorkMATHGoogle Scholar
  21. 21.
    Wang YC, Chiu YC, Hung YP (2011) Optimization of multi-task turning operations under minimal tool waste consideration. Robot Comput Integr Manuf 27(4):674–680.  https://doi.org/10.1016/j.rcim.2010.12.003 CrossRefGoogle Scholar
  22. 22.
    Franco G, Betti R, Lus H (2004) Identification of structural systems using an evolutionary strategy. J Eng Mech 130(10):1125–1139.  https://doi.org/10.1061/(ASCE)0733-9399(2004)130:10(1125) CrossRefGoogle Scholar
  23. 23.
    Bäck T, Schwefel HP (1993) An overview of evolutionary algorithms for parameter optimization. Evol Comput 1(1):1–23.  https://doi.org/10.1162/evco.1993.1.1.1 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Industrial Engineering and Systems ManagementFeng Chia UniversityTaichungTaiwan
  2. 2.Department of Industrial and Information Systems EngineeringChonbuk National UniversityJeonjuSouth Korea
  3. 3.Department of Industrial and Management Systems EngineeringWaseda UniversityTokyoJapan

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