Skip to main content

A comprehensive approach to parameters optimization of energy-aware CNC milling


Cutting parameters are important components in the process of computer numerical control (CNC) machining, and reasonable choice of cutting parameters can significantly affect the energy efficiency. This paper presents a multi-objective parameter optimization method for energy efficiency in CNC milling process. Firstly, the energy consumption composition characteristics and temporal characteristics in CNC milling are analyzed, respectively. The energy model of CNC milling is then established, of which the correlation coefficient is obtained through nonlinear regression fitting. Then a multi-objective optimization model is proposed to take the highest energy efficiency and the minimum production time as the optimization objectives, which is solved based on Tabu search algorithm. Finally, a case study is conducted to validate the proposed multi-objective optimization model and the optimal parameter solutions of maximum energy efficiency and minimum production time is obtained. Moreover, the parametric influence on specific energy consumption and production time are explicitly analyzed. The experiment results show that cutting depth and width are the most influential parameters for specific energy consumption, and spindle speed ranks the first for the production time.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13



Computerized numerical control


Tabu search


Specific energy consumption


Grey relational analysis


Response surface method


Material removal rate


Material removal volume


Non-deterministic polynomial hard


  1. Balogun, V. A., & Mativenga, P. T. (2013). Modelling of direct energy requirements in mechanical machining processes. Journal of Cleaner Production, 41(2), 179–186.

    Article  Google Scholar 

  2. Bhushan, R. K. (2013). Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites. Journal of Cleaner Production, 39(1), 242–254.

    Article  Google Scholar 

  3. Calvanese, M. L., Albertelli, P., & Matta A., et al. (2013). Analysis of energy consumption in CNC machining centers and determination of optimal cutting conditions. In Proceedings of the 20th CIRP international conference on life cycle engineering (pp. 17-19). Singapore.

  4. Campatelli, G., Lorenzini, L., & Scippa, A. (2014). Optimization of process parameters using a response surface method for minimizing power consumption in the milling of carbon steel. Journal of Cleaner Production, 66(2), 309–316.

    Article  Google Scholar 

  5. Camposeco-Negrete, C. (2013). Optimization of cutting parameters for minimizing energy consumption in turning of AISI 6061 T6 using Taguchi methodology and ANOVA. Journal of Cleaner Production, 53(16), 195–203.

    Article  Google Scholar 

  6. Carcangiu, S., Fanni, A., & Montisci, A. (2008). Multiobjective Tabu search algorithms for optimal design of electromagnetic devices. IEEE Transactions on Magnetics, 44(6), 970–973.

    Article  Google Scholar 

  7. Chelouah, R., & Siarry, P. (2000). Tabu search applied to global optimization. European Journal of Operational Research, 123(2), 256–270.

    Article  Google Scholar 

  8. Energy Consumption Survey (MECS) (2013) Total consumption of electricity by manufacturing industry and region. Technical report, US Energy Information Administration.

  9. Gutowski, T., Dahmus, J., & Thiriez, A. (2006). Electrical energy requirements for manufacturing processes. In Proceedings of 13th CIRP international conference on life cycle engineering. Belgium: Leuven, May 31– June 2.

  10. Hanafi, I., Khamlichi, A., Cabrera, F. M., et al. (2012). Optimization of cutting conditions for sustainable machining of PEEK-CF30 using tin tools. Journal of Cleaner Production, 33(8), 1–9.

    Article  Google Scholar 

  11. Hedberg, E. C., & Ayers, S. (2015). The power of a paired t test with a covariate. Social Science Research, 50, 277–291.

    Article  Google Scholar 

  12. Hu, S. H., Liu, F., He, Y., et al. (2010). Characteristics of additional load losses of spindle system of machine tools. Journal of Advanced Mechanical Design, Systems and Manufacturing, 4(7), 1221–1233.

    Article  Google Scholar 

  13. Kant, G., & Sangwan, K. S. (2014). Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining. Journal of Cleaner Production, 83, 151–164.

    Article  Google Scholar 

  14. Kara, S., & Li, W. (2011). Unit process energy consumption models for material removal processes. CIRP Annals Manufacturing Technology, 60(1), 37–40.

    Article  Google Scholar 

  15. Kong, D., Choi, S., Yasui, Y., et al. (2011). Software-based tool path evaluation for environmental sustainability. Journal of Manufacturing Systems, 30(4), 241–247.

    Article  Google Scholar 

  16. Kuram, E., Ozcelik, B., Bayramoglu, M., et al. (2013). Optimization of cutting fluids and cutting parameters during end milling by using D-optimal design of experiments. Journal of Cleaner Production, 42(3), 159–166.

    Article  Google Scholar 

  17. Li, L., Liu, F., Chen, B., & Li, C. B. (2015). Multi-objective optimization of cutting parameters in sculptured parts machining based on neural network. Journal of Intelligent Manufacturing, 26(5), 891–898.

    Article  Google Scholar 

  18. Li, C. B., Tang, Y., Cui, L. G., et al. (2013). A quantitative approach to analyze carbon emissions of CNC-based machining systems. Journal of Intelligent Manufacturing, 26(5), 1–12.

    Google Scholar 

  19. Liu, F., Xu, Z. J., & Dan, B. (1995). Energy performance of mechanical processing system and application. Beijing: China Machine Press.

    Google Scholar 

  20. Li, W., Zein, A., Kara, S., & Herrmann, C. (2011). An investigation into fixed energy consumption of machine tools. In J. Hesselbach & C. Herrmann (Eds.), Glocalized solutions for sustainability in manufacturing (pp. 268–273). Berlin: Springer.

    Chapter  Google Scholar 

  21. Mori, M., Fujishima, M., Inamasu, Y., et al. (2011). A study on energy efficiency improvement for machine tools. CIRP Annals Manufacturing Technology, 60(1), 145–148.

    Article  Google Scholar 

  22. NBSC. (2010). China statistical yearbook. Beijing, China Statistics Press.

  23. Rajemi, M. F., Mativenga, P. T., & Aramcharoen, A. (2010). Sustainable machining: Selection of optimum turning conditions based on minimum energy considerations. Journal of Cleaner Production, 18, 1059–1065.

    Article  Google Scholar 

  24. Salonitis, K., & Ball, P. (2013). Energy efficient manufacturing from machine tools to manufacturing systems. Procedia Cirp, 7(12), 634–639.

    Article  Google Scholar 

  25. Simoneau, A., & Meehan, J. (2013). The impact of machining parameters on peak power and energy consumption in CNC end milling. Energy and Power, 3(5), 85–90.

    Google Scholar 

  26. Valera, H. Y., & Bhavsar, S. N. (2014). Experimental investigation of surface roughness and power consumption in turning operation of EN 31 alloy steel. Procedia Technology, 14, 528–534.

    Article  Google Scholar 

  27. Velchev, S., Kolev, I., Ivanov, K., et al. (2014). Empirical models for specific energy consumption and optimization of cutting parameters for minimizing energy consumption during turning. Journal of Cleaner Production, 80, 139–149.

    Article  Google Scholar 

  28. Wang, Q., Liu, F., & Wang, X. (2013). Multi-objective optimization of machining parameters considering energy consumption. International Journal of Advanced Manufacturing Technology, 71(5–8), 1133–1142.

    Google Scholar 

  29. Yi, Q., Tang, Y., Li, C. B., & Li, P. Y. (2013). Optimization of CNC machine processing parameters for low carbon manufacturing. Proceedings of IEEE 9th conference on automation science and engineering (pp. 498–503). Wisconsin, USA: Madison.

  30. Yoon, H. S., Lee, J. Y., Kim, M. S., et al. (2014). Empirical power-consumption model for material removal in three-axis milling. Journal of Cleaner Production, 78, 54–62.

    Article  Google Scholar 

  31. Yoon, H. S., Moon, J. S., Pham, M. Q., Lee, G. B., & Ahn, S. H. (2013). Control of machining parameters for energy and cost savings in micro-scale drilling of PCBs. Journal of Cleaner Production, 54, 41–48.

    Article  Google Scholar 

Download references


This work was supported in part by the National High-Tech R&D Program of China under Grant 2014AA041506, and the National Natural Science Foundation of China (NSFC) under Grant 51475059.

Author information



Corresponding author

Correspondence to Congbo Li.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, C., Li, L., Tang, Y. et al. A comprehensive approach to parameters optimization of energy-aware CNC milling. J Intell Manuf 30, 123–138 (2019).

Download citation


  • CNC milling
  • Cutting parameters
  • Energy efficiency
  • Multi-objective optimization