Skip to main content
Log in

Exergy-based Energy Efficiency Evaluation Model for Machine Tools Considering Thermal Stability

  • Regular Paper
  • Published:
International Journal of Precision Engineering and Manufacturing-Green Technology Aims and scope Submit manuscript

Abstract

Machine tools, as the extensively used basic equipment of manufacturing industry, are characterized by intensive and inefficient energy consumption. With the launch and implementation of ISO 14955-1, energy efficiency has become an important criterion for machine tool evaluation. However, most ongoing research on energy efficiency evaluation of machine tools emphasizes on workpiece material removal energy efficiency and rarely considers energy consumption needed to ensure machining accuracy and accuracy consistency, especially energy consumption for thermal stability control of machine tools. In light of this, an exergy analysis based approach is presented to assess the comprehensive energy efficiency of machine tools, including energy consumption for material removal and thermal stability control. The key performance indexes of exergy efficiency, exergy destruction, and specific exergy consumption are analyzed. The feasibility of the proposed approach was demonstrated by a case study, in which the comprehensive energy efficiency of a machine tool was found to be 21.57% instead of 14.38% of material removal energy efficiency. The proposed method is more effective to evaluate the comprehensive energy efficiency, to support designers to design high-efficient machine tool and users to operate machine tool for green and precision machining.

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.

Institutional subscriptions

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

Similar content being viewed by others

Abbreviations

A surf :

surface area of machine tool shell

A nc :

The area of natural heat convection

c ho :

Specific heat of hydraulic oil

c co :

Specific heat of cooling fluid

c lu :

Specific heat of lubricant

c ca :

Specific heat of compressed air

ca :

Specific heat capacity of air

E elec,MT :

Electrical energy input of machine tool drives

E elec,PD :

Electrical energy input of peripheral devices

E MR :

Material removal energy

E loss :

Electrical energy loss

Ėx dest :

Exergy destruction rate

Ėx elec,MT :

Electrical exergy rate of machine tool drives

Ėx elec,PD :

Electrical exergy rate of peripheral devices

Ėx MR :

Material removal exergy rate

Ėx mass,ca :

Flow exergy rate of compressed air

Ėx mass,co :

Flow exergy rate of coolant

Ėx mass,ha :

Flow exergy rate of hot air

Ėx mass,ho :

Flow exergy rate of hydraulic oil

Ėx mass,lu :

Flow exergy rate of lubricant

Ėx nc :

Thermal exergy output rate of heat transfer by natural convection

h nc :

Convection heat transfer coefficient

ca :

Mass low rate of compressed air

co :

Mass low rate of cooling fluid

ha :

Mass low rate of hot air

ho :

Mass low rate of hydraulic oil

lu :

Mass low rate of lubricant

P elec,feed :

Electrical power input of feed motor

P elec,sp :

Electrical power input of spindle

p 0 :

Ambient air pressure

p ca :

Compressed air pressure

hr :

Heat transfer rate of radiation

nc :

Heat transfer rate of natural convection

R g :

Gas constant

T 0 :

Ambient air temperature

T ca :

Temperature of compressed air

T co :

Temperature of cooling fluid

T ha :

Temperature of hot air

T ho :

Temperature of hydraulic oil

T lu :

Temperature of lubricant

T surf :

Average temperature of machine tool surface

y PD :

Exergy destruction ratio of the kth peripheral device

y MT :

Exergy destruction ratio of machine tool drives

ε surf :

Emissivity of machine tool shell

ε PD :

Exergy efficiency of peripheral device

ε MT :

Exergy efficiency of machine tool drives

ε tot :

Total exergy efficiency

σ :

Stefan–Boltzmann constant

η I :

Traditional energy efficiency

η II :

Comprehensive energy efficiency

References

  1. Duflou, J. R., Sutherland, J. W., Dornfeld, D., Herrmann, C., Jeswiet, J., Kara, S., et al. (2012). Towards energy and resource efficient manufacturing: A processes and systems approach. CIRP Annals—Manufacturing Technology, 61(2), 587–609.

    Google Scholar 

  2. D-y, Jang, Jung, J., & Seok, J. (2016). Modeling and parameter optimization for cutting energy reduction in MQL milling process. International Journal of Precision Engineering and Manufacturing-Green Technology, 3(1), 5–12.

    Google Scholar 

  3. Cai, W., Liu, F., Zhang, H., Liu, P. J., & Tuo, J. B. (2017). Development of dynamic energy benchmark for mass production in machining systems for energy management and energy-efficiency improvement. Applied Energy, 202, 715–725.

    Google Scholar 

  4. Oh, N.-S., Woo, W.-S., & Lee, C.-M. (2018). A study on the machining characteristics and energy efficiency of Ti-6Al-4V in laser-assisted trochoidal milling. International Journal of Precision Engineering and Manufacturing-Green Technology, 5(1), 37–45.

    Google Scholar 

  5. Woo, W. S., & Lee, C. M. (2018). A study on the optimum machining conditions and energy efficiency of a laser-assisted fillet milling. International Journal of Precision Engineering and Manufacturing-Green Technology, 5(5), 593–604.

    Google Scholar 

  6. Deng, Z., Lv, L., Huang, W., & Shi, Y. (2019). A high efficiency and low carbon oriented machining process route optimization model and its application. International Journal of Precision Engineering and Manufacturing-Green Technology, 6(1), 23–41.

    Google Scholar 

  7. Gutowski T., Dahmus J., & Thiriez A. (2006). Electrical energy requirements for manufacturing processes. In 13th CIRP International Conference on Life Cycle Engineering. Belgium: Leuven.

  8. Cao, H. J., Li, H. C., Cheng, H. Q., Luo, Y., Yin, R. X., & Chen, Y. P. (2012). A carbon efficiency approach for life-cycle carbon emission characteristics of machine tools. Journal of Cleaner Production, 37, 19–28.

    Google Scholar 

  9. Züst, S., Züst, R., Schudeleit, T., & Wegener, K. (2016). Development and application of an eco-design tool for machine tools. Procedia Cirp, 48, 431–436.

    Google Scholar 

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

    Google Scholar 

  11. Balogun, V. A., Edem, I. F., Adekunle, A. A., & Mativenga, P. T. (2016). Specific energy based evaluation of machining efficiency. Journal of Cleaner Production, 116, 187–197.

    Google Scholar 

  12. Cai, W., Liu, F., & Hu, S. H. (2018). An analytical investigation on energy efficiency of high-speed dry-cutting CNC hobbing machines. International Journal of Sustainable Engineering, 11(6), 412–419.

    Google Scholar 

  13. Liu, Z. Y., & Guo, Y. B. (2018). A hybrid approach to integrate machine learning and process mechanics for the prediction of specific cutting energy. CIRP Annals—Manufacturing Technology, 67(1), 57–60.

    Google Scholar 

  14. Ghosh, S., Chattopadhyay, A. B., & Paul, S. (2008). Modelling of specific energy requirement during high-efficiency deep grinding. International Journal of Machine Tools and Manufacturing, 48(11), 1242–1253.

    Google Scholar 

  15. Heinzel, C., & Kolkwitz, B. (2019). The impact of fluid supply on energy efficiency and process performance in grinding. CIRP Annals—Manufacturing Technology. https://doi.org/10.1016/j.cirp.2019.03.023.

    Article  Google Scholar 

  16. Bryan, J. (1990). International status of thermal error research. CIRP Annals—Manufacturing Technology, 39, 645–656.

    Google Scholar 

  17. Mayr, J., Jedrzejewski, J., Uhlmann, E., Alkan, Donmez M., Knapp, W., Härtig, F., et al. (2012). Thermal issues in machine tools. CIRP Annals—Manufacturing Technology, 61(2), 771–791.

    Google Scholar 

  18. Goindi, G. S., & Sarkar, P. (2017). Dry machining: A step towards sustainable machining—challenges and future directions. Journal of Cleaner Production, 165, 1557–1571.

    Google Scholar 

  19. Guerrini, G., Landi, E., Peiffer, K., & Fortunato, A. (2018). Dry grinding of gears for sustainable automotive transmission production. Journal of Cleaner Production, 176, 76–88.

    Google Scholar 

  20. Li, B. J., Cao, H. J., Yang, X., Jafar, S., & Zeng, D. (2018). Thermal energy balance control model of motorized spindle system enabling high-speed dry hobbing process. Journal of Manufacturing Processes, 35, 29–39.

    Google Scholar 

  21. Yang, X., Cao, H. J., Li, B. J., Jafar, S., & Zhu, L. B. (2018). A thermal energy balance optimization model of cutting space enabling environmentally benign dry hobbing. Journal of Cleaner Production, 172, 2323–2335.

    Google Scholar 

  22. Shi, H., Ma, C., Jun, Y., Zhao, L., Mei, X. S., & Gong, G. F. (2015). Investigation into effect of thermal expansion on thermally induced error of ball screw feed drive system of precision machine tools. International Journal of Machine Tools and Manufacture, 97, 60–71.

    Google Scholar 

  23. Regel, J., Du, X., Bräunig, M., Wittstock, V., & Putz, M. (2018). Evaluation of thermo-energetic behavior for demand-oriented operating of machine tool cooling systems. Procedia Manufacturing, 21, 213–220.

    Google Scholar 

  24. Cengel, Y. A., & Boles, M. A. (2010). Thermodynamics: An Engineering Approach (7th ed.). New York: McGraw-Hill Education.

    Google Scholar 

  25. Shin, J., Yoon, S., & Kim, J. K. (2015). Application of exergy analysis for improving energy efficiency of natural gas liquids recovery processes. Applied Thermal Engineering, 75, 967–977.

    Google Scholar 

  26. Ahamed, J. U., Madlool, N. A., Saidur, R., Shahinuddin, M. I., Kamyar, A., & Masjuki, H. H. (2012). Assessment of energy and exergy efficiencies of a grate clinker cooling system through the optimization of its operational parameters. Energy, 46, 664–674.

    Google Scholar 

  27. Kaushik, S. C., Manikandan, S., & Hans, R. (2015). Energy and exergy analysis of thermoelectric heat pump system. International Journal of Heat and Mass Transfer, 86, 843–852.

    Google Scholar 

  28. Hosseinzadeh, M., Sardarabadi, M., & Passandideh-Fard, M. (2018). Energy and exergy analysis of nanofluid based photovoltaic thermal system integrated with phase change material. Energy, 147, 636–647.

    Google Scholar 

  29. Koroglu, T., & Sogut, O. S. (2018). Conventional and advanced exergy analyses of a marine steam power plant. Energy, 163, 392–403.

    Google Scholar 

  30. Zhang, Q., Yi, H. N., Yu, Z. H., Gao, J. T., Wang, X. Z., Lin, H. Y., et al. (2018). Energy-exergy analysis and energy efficiency improvement of coal-fired industrial boilers based on thermal test data. Applied Thermal Engineering, 144, 614–627.

    Google Scholar 

  31. Bühler, F., Nguyen, T. V., Jensen, J. K., Holm, F. M., & Elmegaard, B. (2018). Energy, exergy and advanced exergy analysis of a milk processing factory. Energy, 162, 576–592.

    Google Scholar 

  32. Sharifzadeh, M., Ghazikhani, M., & Niazmand, H. (2018). Temporal exergy analysis of adsorption cooling system by developing non-flow exergy function. Applied Thermal Engineering, 139, 409–418.

    Google Scholar 

  33. Gutowski, T. G., Branham, M. S., Dahmus, J. B., Jones, A. J., & Alexandre, T. (2009). Thermodynamic analysis of resources used in manufacturing processes. Environmental Science and Technology, 43, 1584–1590.

    Google Scholar 

  34. Wang F.J., Chang T.B., Chiang W.M., & Lee H.C. (2008). Exergy Analyses of a Machine Tool Cooler System Using Eco-friendly Refrigerants. In: 4th International Conference on Cryogenic and Refrigeration Engineering, Shanghai, China.

  35. Zhu, L. B., Cao, H. J., Huang, H. H., & Yang, X. (2017). Exergy analysis and multi-objective optimization of air cooling system for dry machining. The International Journal of Advanced Manufacturing Technology, 93, 3175–3188.

    Google Scholar 

  36. Li, B. J., Cao, H. J., Liu, H., Zeng, D., & Chen, E. H. (2019). Exergy efficiency optimization model of motorized spindle system for high-speed dry hobbing. The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-019-04134-x.

    Article  Google Scholar 

  37. Zhou, L. R., Li, J. F., Li, F. Y., Meng, Q., Li, J., & Xu, X. S. (2016). Energy consumption model and energy efficiency of machine tools: A comprehensive literature review. Journal of Cleaner Production, 112, 3721–3734.

    Google Scholar 

  38. Ma, C., Yang, J., Zhao, L., Mei, X. S., & Hu, S. (2015). Simulation and experimental study on the thermally induced deformations of high-speed spindle system. Applied Thermal Engineering, 86, 251–268.

    Google Scholar 

  39. Li, Y., Zhao, W. H., Wu, W. W., Lu, B. H., & Chen, Y. B. (2014). Thermal error modeling of the spindle based on multiple variables for the precision machine tool. International Journal of Advanced Manufacturing Technology, 72, 1415–1427.

    Google Scholar 

  40. Atmaca, A., & Yumrutaş, R. (2014). Thermodynamic and exergoeconomic analysis of a cement plant: Part II—Application. Energy Conversion and Management, 79, 799–808.

    Google Scholar 

  41. Chien, C. H., & Jang, J. Y. (2008). 3-D numerical and experimental analysis of a built-in motorized high-speed spindle with helical water cooling channel. Applied Thermal Engineering, 28, 2327–2336.

    Google Scholar 

  42. Yang, K., Zhu, N., Ding, Y., Chang, C., Wang, D. Q., & Yuan, T. H. (2019). Exergy and exergoeconomic analyses of a combined cooling, heating, and power (CCHP) system based on dual-fuel of biomass and natural gas. Journal of Cleaner Production, 206, 893–906.

    Google Scholar 

  43. Liu, P. J., Liu, F., & Qiu, H. (2017). A novel approach for acquiring the real-time energy efficiency of machine tools. Energy, 121, 524–532.

    Google Scholar 

  44. Kolar, M., Vyroubal, J., & Smolik, J. (2016). Analytical approach to establishment of predictive models of power consumption of machine tools’ auxiliary units. Journal of Cleaner Production, 137, 361–369.

    Google Scholar 

  45. Incropera, F. P., Dewitt, D. P., Bergman, T. L., & Lavine, A. S. (2007). Fundamentals of Heat and Mass Transfer. Hoboken: Wiley.

    Google Scholar 

  46. Liu, T., Gao, W. G., Zhang, D. W., Zhang, Y. F., Chang, W. F., Liang, C. M., et al. (2017). Analytical modeling for thermal errors of motorized spindle unit. International Journal of Machine Tools and Manufacture, 112, 53–70.

    Google Scholar 

  47. Atmaca, A., & Yumrutaş, R. (2014). Thermodynamic and exergoeconomic analysis of a cement plant: Part I—Methodology. Energy Conversion and Management, 79, 790–798.

    Google Scholar 

  48. Lin, J., Bui, D. T., Wang, R., & Chua, K. J. (2018). On the exergy analysis of the counter-flow dew point evaporative cooler. Energy, 165, 958–971.

    Google Scholar 

  49. Cao, H. J., Zhu, L. B., Li, X. G., Chen, P., & Chen, Y. P. (2016). Thermal error compensation of dry hobbing machine tool considering workpiece thermal deformation. The International Journal of Advanced Manufacturing Technology, 86(5–8), 1739–1751.

    Google Scholar 

  50. Yang, X., Cao, H. J., Chen, Y. P., Zhu, L. B., & Li, B. J. (2017). An analytical model of chip heat-carrying capacity for high-speed dry hobbing based on 3D chip geometry. International Journal of Precision Engineering and Manufacturing, 18(2), 245–256.

    Google Scholar 

  51. Chen, Y. P., Cao, H. J., Li, X. G., & Chen, P. (2016). The model of spatial forming with multi-cutting-edge for cylindrical gear hobbing and its application. Journal of Mechanical Engineering, 52, 176–183.

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant number 51975076); the National Key Research and Development Program of China (Grant number 2018YFB2002201); the Project of International Cooperation and Exchanges NSFC (Grant number 51861165202); and the Fundamental Research Funds for the Central Universities of China (Grant number 2018CDJDC0001). The authors gratefully acknowledge the reviewers and editors for their insightful comments.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Huajun Cao.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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

Li, B., Cao, H., Hon, B. et al. Exergy-based Energy Efficiency Evaluation Model for Machine Tools Considering Thermal Stability. Int. J. of Precis. Eng. and Manuf.-Green Tech. 8, 423–434 (2021). https://doi.org/10.1007/s40684-020-00204-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40684-020-00204-8

Keywords

Navigation