Skip to main content

Advertisement

SpringerLink
Log in

An energy consumption optimization strategy for CNC milling

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

Abstract

Energy saving is a problem of growing importance due to the low-energy utilization and increasing environmental awareness. However, the challenge of energy optimization is to assure the accuracy of the energy forecast model. As a full understanding of material-cutting energy is a key aspect of machining process, energy modeling is essential for its optimization. This study proposes a specific energy calculation model and an optimization model to predict and optimize the electrical energy consumed by a three-axis milling machine. In this model, the impacts of cutting parameters on energy are fully considered and the MATLAB optimization toolbox is used for the solution. To assess the usefulness and practicality of the proposed method, an experimental study with a CNC milling machine is presented. The results demonstrate the effectiveness of energy improvement of this method based on optimizing the spindle speed in milling process. Additionally, the predictive accuracy of the energy model is above 90 %, which offers a viable approach for achieving higher machining efficiency.

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. Bulmus SC, Zhu SX, Teunter R (2014) Competition for cores in remanufacturing. Eur J Oper Res 233(1):105–113

    Article  MathSciNet  MATH  Google Scholar 

  2. Fang K, Uhan N, Zhao F, Sutherland JW (2011) A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction. J Manuf Syst 30:234–240

    Article  Google Scholar 

  3. He Y, Liu B, Zhang X, Gao H, Liu X (2012) A modeling method of task-oriented energy consumption for machining manufacturing system. J Clean Prod 23:167–174

    Article  Google Scholar 

  4. Kant G, Sangwan KS (2014) Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining. J Clean Prod 83:151–164

    Article  Google Scholar 

  5. Li W, Kara W (2011) An empirical model for predicting energy consumption of manufacturing processes: a case of turning process. Proceedings of the Institution of Mechanical Engineers, Part B: J Eng Manuf 225(9):1636–1646

    Article  Google Scholar 

  6. Soete WD, Boone L, Willemse F (2014) Environmental resource foot-printing of drug manufacturing: effects of scale-up and tablet dosage. Resour Conserv Recycl 91:82–88

    Article  Google Scholar 

  7. Egilmez G, Kucukvar M, Tatari O, Bhutta MKS (2014) Erratum: supply chain sustainability assessment of the U.S. food manufacturing sectors: a life cycle based frontier approach. Resour Conserv Recycl 86:147–148

    Article  Google Scholar 

  8. Kreiger MA, Shonnard DR, Pearce JM (2013) Life cycle analysis of silane recycling in amorphous silicon-based solar photovoltaic manufacturing. Resour Conserv Recycl 70:44–49

    Article  Google Scholar 

  9. Lam JSL, Lai KH (2015) Developing environmental sustainability by ANP-QFD approach: the case of shipping operations. J Clean Prod 105:275–284

    Article  Google Scholar 

  10. Liu F, Liu S (2012) Multi-period energy model of electro-mechanical main driving system during the service process of machine tools. J Mech Eng 48(21):132–140

    Article  Google Scholar 

  11. Wang Q, Liu F, Wang X (2014) Multi-objective optimization of machining parameters considering energy consumption. Int J Manuf Technol 71:1133–1142

    Article  Google Scholar 

  12. Liow JL (2009) Mechanical micromachining: a sustainable micro-device manufacturing approach. J Clean Prod 17(7):662–667

    Article  Google Scholar 

  13. Yang Y, Li XY, Gao L, Shao XY (2013) Modeling and impact factors analyzing of energy consumption in CNC face milling using GRASP gene expression programming. Int J Adv Manuf Technol. doi:10.1007/s00170-013-5017-7

  14. Camposeco-Negrete C, Miranda-Valenzuela C (2016) Optimization of cutting parameters to minimize energy consumption during turning of AISI 1018 steel at constant material removal rate using robust design. Int J Adv Manuf Technol 83:1341–1347

    Article  Google Scholar 

  15. Mori M, Fujishima M, Inamasu Y, Oda Y (2011) A study on energy efficiency improvement for machine tools. CIRP Ann 60(1):145–148

    Article  Google Scholar 

  16. Xiao Y, Zhang H, Jiang Z (2015) An approach for blank dimension design considering energy consumption. Int J Adv Manuf Technol 10:1–7

    Google Scholar 

  17. Quintana G, Ciurana J, Ribatallada J (2011) Modeling power consumption in ball end milling operations with artificial neural network. Mater Manuf Process 26(5):746–756

    Article  Google Scholar 

  18. Newman ST, Nassehi A, Imani-Asrai R, Dhokia V (2012) Energy efficient process planning for CNC machining. CIRP J Manuf Sci Technol 5(2):127–136

    Article  Google Scholar 

  19. Tolio T, Sacco M, Terkaj W, Urgo M (2013) Virtual factory: an integrated framework for manufacturing systems design and analysis. Proc CIRP 7:25–30

    Article  Google Scholar 

  20. Camposeco-Negrete C (2013) Optimization of cutting parameters for minimizing energy consumption in turning of AISI 6061 T6 using Taguchi methodology and ANOVA. J Clean Prod 53(16):195–203

    Article  Google Scholar 

  21. Zhang Y, Ge L (2015) Method for process planning optimization with energy efficiency consideration. Int J Adv Manuf Technol 77:2197–2207

    Article  Google Scholar 

  22. Cai Y, Liu Z, Shi Z, Song Q, Wan Y (2015) Optimization of machining parameters for micro-machining nozzle based on characteristics of surface roughness. Int J Adv Manuf Technol 80:1403–1410

    Article  Google Scholar 

  23. Mativenga PT, Rajemi MF (2011) Calculation of optimum cutting parameters based on minimum energy footprint. CIRP Ann 60:149–152

    Article  Google Scholar 

  24. Zulaika JJ, Campa FJ, Lacalle LNLD (2011) An integrated process—machine approach for designing productive and light weight milling machines. Int J Mach Tools Manuf 51(7):591–604

    Article  Google Scholar 

  25. Diaz N, Redelsheimer E, Dornfeld D (2011) Energy consumption characterization and reduction strategies for milling machine tool use. Proc Ann 18th CIRP Int Conf Life Cycle Eng :263–267

  26. Diaz N, Helu M, Jarvis A, Tönissen S (2009) Strategies for minimum energy operation for precision machining. Proc MTTRF 2009 Ann: 47–50. Shanghai, China

  27. Gutowski T, Murphy C, Allen D (2005) Environmentally benign manufacturing: observations from Japan, Europe and the United States. J Clean Prod 13(1):1–17

    Article  Google Scholar 

  28. Gutowski T, Dahmus J, Thiriez A (2006) Electrical energy requirements for manufacturing processes. Proc Ann13th CIRP Int Conf Life Cycle Eng: 623–627

  29. Jia S, Tang R, Lv J, Zhang Z, Yuan Q (2015) Energy modeling for variable material removal rate machining process: an end face turning case. Int J Adv Manuf Technol 12:1–14

    Google Scholar 

  30. Balogun VA, Mativenga PT (2013) Modelling of direct energy requirements in mechanical machining processes. J Clean Prod 41(2):179–186

    Article  Google Scholar 

  31. Li L, Yan J, Xing Z (2013) Energy requirements evaluation of milling machines based on thermal equilibrium and empirical modelling. J Clean Prod 52:113–121

    Article  Google Scholar 

  32. Hu S, Liu F, He Y (2012) No-load energy parameter characteristics of computerized numerical control machine tool main transmission system. Comput Integr Manuf Syst 18(2):326–331

    Google Scholar 

  33. Fei L, Xu Z (1995) The energy characteristics of machine tool’s system and its application. China Machine Press, BeiJing in Chinese

    Google Scholar 

  34. Gutowski TG, Liow JYH, Sekulic DP (2010) Minimum exergy requirements for the manufacturing of carbon nanotubes. Int Symp Sust Syst Technol (ISSST2010). Washington, DC

  35. Bayoumi AE, Yücesan G, Hutton DV (1994) On the closed form mechanistic modeling of milling: specific cutting energy, torque and power. J Mater Eng Perform 3(1):151–158

    Article  Google Scholar 

  36. Zhong Q, Tang R, Lv J, Jia S, Jin M (2016) Evaluation on models of calculating energy consumption in metal cutting processes: a case of external turning process. Int J Adv Manuf Technol 82:2087–2099

    Article  Google Scholar 

  37. Li W, Zein A, Kara S, Herrmann C (2011) Glocalized solutions for sustainability in manufacturing. In: Hesselbach J, Herrmann C, (eds) Glocalized Solutions for Sustainability in Manufacturing: Proc 18th CIRP Int Conf Life Cycle Eng, pp 268–273

Download references

Author information

Authors and Affiliations

  1. College of Machinery and Automation, Wuhan University of Science and Technology, Wuhan, 430081, China

    Feng Ma, Hua Zhang, Huajun Cao & K. K. B. Hon

  2. College of Machinery and Automation, Chongqing University, Chongqing, 430081, China

    Huajun Cao

  3. School of Engineering, University of Liverpool, Liverpool, L69 3GH, UK

    K. K. B. Hon

Authors
  1. Feng Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hua Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huajun Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. K. K. B. Hon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Feng Ma.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, F., Zhang, H., Cao, H. et al. An energy consumption optimization strategy for CNC milling. Int J Adv Manuf Technol 90, 1715–1726 (2017). https://doi.org/10.1007/s00170-016-9497-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-016-9497-0

Keywords

Search

Navigation