Skip to main content

Advertisement

Log in

A generalized analysis of energy saving strategies through experiment for CNC milling machine tools

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

Abstract

This paper proposes the elaboration model of energy requirement prediction taking into account the power of standby, spindle rotation in non-load, feeding, and rapid movement in X, Y, Z+, and Z− axially, and specific energy consumption (SEC) in the X and Y cutting directions, respectively, which could not be considered complete in other models. Each part energy of specific machine tools could be obtained through little experiments for identifying the relationship between energy and tool path with cutting parameters. The method is validated by 27 trial cutting experiment in X and Y cutting directions in the VMC850E machine; the results show that the SEC in the X and Y cutting directions is different. Moreover, it is found that spindle power should be piecewise linear representation according to spindle speed characteristic, due to the correlation coefficient of power model only has 25.45% without segmented. Additionally, the correlation coefficient of the improved SEC model could reach more than 99.98% in each segment. The contribution of this paper is mainly the elaboration energy consumption model considering the cutting direction, which is an efficient approach for predicting energy consumption through tool path to achieve sustainable production in manufacturing sectors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig.1
Fig.2
Fig.3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

All the data have been presented in the manuscript.

References

  1. Zhou L, Li J, Li F, Meng Q, Li J, Xu X (2016) Energy consumption model and energy efficiency of machine tools: a comprehensive literature review. J Clean Prod 112:3721–3734

    Article  Google Scholar 

  2. Zhao GY, Liu ZY, He Y, Cao HJ, Guo YB (2017) Energy consumption in machining: classification, prediction, and reduction strategy. Energy 133:142–157

    Article  Google Scholar 

  3. Gutowski T, Dahmus J, Thiriez A (2006) Electrical energy requirements for manufacturing processes. In: 13th CIRP international conference on life cycle engineering, Leuven, Belgium, May 31st-June 2nd, pp 560–564

  4. Diaz N, Redelsheimer E, Dornfeld D (2011) Energy consumption characterization and reduction strategies for milling machine tool use. In: Hesselbach J, Herrmann C (eds) Glocalized Solutions for Sustainability in Manufacturing. Springer, Berlin Heidelberg, pp 263–267

    Chapter  Google Scholar 

  5. Kara S, Li W (2011) Unit process energy consumption models for material removal processes. CIRP Ann Manuf Technol 60(1):37–40

    Article  Google Scholar 

  6. 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

    Article  Google Scholar 

  7. 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 

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

    Article  Google Scholar 

  9. 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

    Article  Google Scholar 

  10. Balogun VA, Aramcharoen A, Mativenga PT, Chuan SK (2013) Impact of machine tools on the direct energy and associated carbon emissions for a standardized NC toolpath. In: 20th CIRP International Conference on Life Cycle Engineering, Singapore, April 17–19, pp 197–202

  11. He Y, Liu F, Wu T, Zhong FP, Peng B (2012) Analysis and estimation of energy consumption for numerical control machining. Proc Inst Mech Eng B J Eng Manuf 226(2):255–266

    Article  Google Scholar 

  12. Behrendt T, Zein A, Min S (2012) Development of an energy consumption monitoring procedure for machine tools. CIRP Ann Manuf Technol 61(1):43–46

    Article  Google Scholar 

  13. Altnta RS, Kahya M, Zgür H (2016) Modelling and optimization of energy consumption for feature based milling. Int J Adv Manuf Technol 86:3345–3363

    Article  Google Scholar 

  14. Moradnazhad M, Unver HO (2017) Energy consumption characteristics of turn-mill machining. Int J Adv Manuf Technol 91:1991–2016

    Article  Google Scholar 

  15. Hu L, Tang R, Cai W, Feng Y, Ma X (2019) Optimisation of cutting parameters for improving energy efficiency in machining process. Robot Comput Integr Manuf 59:406–416

    Article  Google Scholar 

  16. Shin SJ, Woo J, Rachuri S (2017) Energy efficiency of milling machining: component modeling and online optimization of cutting parameters. J Clean Prod 161:12–29

    Article  Google Scholar 

  17. Chen X, Li C, Tang Y, Xiao Q (2018) An internet of things based energy efficiency monitoring and management system for machining workshop. J Clean Prod 199:957–968

    Article  Google Scholar 

  18. He Y, Wu P, Li Y, Wang Y, Wang Y (2020) A generic energy prediction model of machine tools using deep learning algorithms. Appl Energy 275:115402

    Article  Google Scholar 

  19. Xu L, Huang C, Li C, Wang J, Liu H, Wang X (2020) A novel intelligent reasoning system to estimate energy consumption and optimize cutting parameters toward sustainable machining. J Clean Prod 261:121160

    Article  Google Scholar 

  20. Edem IF, Balogun VA, Nkanang BD, Mativenga PT (2019) Software analyses of optimum toolpath strategies from computer numerical control (CNC) codes. Int J Adv Manuf Technol 103:997–1007

    Article  Google Scholar 

  21. Moreira LC, Li WD, Lu X, Fitzpatrick ME (2019) Sustainable machining process: qualitative analysis and energy efficiency optimization. In: Li W, Wang S (eds) Sustainable Manufacturing and Remanufacturing Management- Process Planning, Optimization and Applications. Coventry, UK, pp 165–189

  22. Akkuş H, Yaka H (2021) Experimental and statistical investigation of the effect of cutting parameters on surface roughness, vibration and energy consumption in machining of titanium 6Al-4V ELI (grade 5) alloy. Measurement 167:108465

    Article  Google Scholar 

Download references

Acknowledgements

Shi Huang, Guozhen Bai, Yilong Wu, and Haohao Guo are thanked for providing technical support during the experiments.

Funding

This research is funded by the National Natural Science Foundation of China Grant No. 51605294

Author information

Authors and Affiliations

Authors

Contributions

Chunhua Feng: conceptualization, methodology, software, validation, writing-original draft, funding acquisition. Xiang Chen: investigation, data curation, software. Jingyang Zhang: investigation, data curation, resources. Yugui Huang: investigation, data curation, resources.

Corresponding author

Correspondence to Chunhua Feng.

Ethics declarations

Ethical approval

Not applicable.

Consent to participate

The authors declare that they all consent to participate this research.

Consent to publish

The authors declare that they all consent to publish the manuscript.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Feng, C., Chen, X., Zhang, J. et al. A generalized analysis of energy saving strategies through experiment for CNC milling machine tools. Int J Adv Manuf Technol 117, 751–763 (2021). https://doi.org/10.1007/s00170-021-07787-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-021-07787-9

Keywords

Navigation