Skip to main content
SpringerLink
Log in

Optimization of machining economics and energy consumption in face milling operations

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

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.

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

References

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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 2

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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. 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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  10. Zein A (2012) Transition towards energy efficiency machine tools. Springer Science & Business Media

  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

    Book  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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):1636–1646

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  19. Isakov E (2003) Engineering formulas for metalcutting, Industrial Press, Inc.

  20. Bäck T (1996) Evolutionary algorithms in theory and practice. Oxford University Press, New York

    MATH  Google Scholar 

  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

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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

    Article  Google Scholar 

Download references

Funding

This study was supported by the National Science Council of Taiwan under contract no. MOST 104-2221-E-035 -031 -MY3.

Author information

Authors and Affiliations

  1. Department of Industrial Engineering and Systems Management, Feng Chia University, No.100, Wenhwa Rd., Seatwen, Taichung, 40724, Taiwan

    Yi-Chi Wang & Wen-Chin Hsueh

  2. Department of Industrial and Information Systems Engineering, Chonbuk National University, Jeonju, South Korea

    Dong-Won Kim

  3. Department of Industrial and Management Systems Engineering, Waseda University, Tokyo, Japan

    Hiroshi Katayama

Authors
  1. Yi-Chi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dong-Won Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hiroshi Katayama
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wen-Chin Hsueh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yi-Chi Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, YC., Kim, DW., Katayama, H. et al. Optimization of machining economics and energy consumption in face milling operations. Int J Adv Manuf Technol 99, 2093–2100 (2018). https://doi.org/10.1007/s00170-018-1848-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-018-1848-6

Keywords

Search

Navigation