Skip to main content

Advertisement

SpringerLink
Log in

Energy Performance Evaluation Method for Machining Systems Towards Energy Saving and Emission Reduction

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

Abstract

Energy performance improvement is a basic significant way of addressing both energy security and environment concerns, which can promote energy-saving and emission-reduction. There are various measures of energy performance, with different purposes and applications. However, there are few models or approaches for measuring and quantifying energy saving potential in machining systems. To better perform the energy performance analysis and evaluating energy saving potential in machining, an energy performance evaluation method based on energy benchmark in machining systems is addressed. Energy performance characteristics is analysed, and some energy performance concepts and indicators are proposed. The energy performance evaluation based on energy benchmark is developed for machining systems. Furthermore, a case study involving the establishment of an energy performance evaluation and energy saving potential for gears in a real machining plant was examined, illustrating the practicability of the proposed method.

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

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

Similar content being viewed by others

References

  1. Cai, W., Li, L., Jia, S., et al. (2020). Task-oriented energy benchmark of machining systems for energy-efficient production[J]. International Journal of Precision Engineering and Manufacturing-Green Technology, 7(1), 205–218.

    Article  Google Scholar 

  2. Chen, G. Q., Wu, X. D., Guo, J., et al. (2019). Global overview for energy use of the world economy: Household-consumption-based accounting based on the world input-output database (WIOD). Energy Economics, 81, 835.

    Article  Google Scholar 

  3. Duflou, J., Sutherland, J., Dornfeld, D., et al. (2012). Towards energy and resource efficient manufacturing: A processes and systems approach. CIRP Annals -Manufacturing Technology, 61(2), 687–609.

    Article  Google Scholar 

  4. IEA (International Energy Agency), Energy efficiency (2017). IEA World Energy Statistics and Balances (database), OECD/IEA, Paris, www.iea.org/statistics/relateddatabases/worldenergystatisticsandbalances/

  5. IEA (2017c), Mobility Model (database), (2017). OECD/IEA, Paris, www.iea.org/etp/etpmodel/transport/ (accessed 25 May 2017).

  6. IEA (2017d), Energy Technology Perspectives, (2017). (Residential Model), OECD/IEA, Paris, www.iea.org/etp/.

  7. Ke, J., Price, L., McNeil, M., et al. (2013). Analysis and practices of energy benchmarking for industry from the perspective of systems engineering. Energy, 54, 32–44.

    Article  Google Scholar 

  8. Hu, L., Cai, W., Shu, L., et al. (2021). Energy optimisation for end face turning with variable material removal rate considering the spindle speed changes. International Journal of Precision Engineering and Manufacturing-Green Technology, 8(2), 625–638.

    Article  Google Scholar 

  9. Liang, C., Feng, H., & Chun, L. (2014). Energy efficiency benchmarking of energy-intensive industries in Taiwan. Energy Conversion and Management, 77, 216–220.

    Article  Google Scholar 

  10. S, Turetskyy A, Loellhoeffel T, , et al. (2020). Machine learning approach for systematic analysis of energy efficiency potentials in manufacturing processes: A case of battery production. CIRP Annals -Manufacturing Technology, 69(1), 21–24.

    Article  Google Scholar 

  11. ISO 14955–1:2014. (2014). Machine tools e environmental evaluation of machine tools -part 1: design methodology for energy-efficient machine tools. International Organization for Standardization (ISO).

  12. European Union. Directive 2012/27/EU of the european parliament and of the council of 25 October 2012 on energy efficiency, amending directives 2009/ 125/EC and 2010/30/EU and repealing directives 2004/8/EC and 2006/32/EC.2012 [accessed: 12.05.2015] Available: http://eur-lex.europa.eu/LexUriServ/LexUriServ.do? uri¼OJ:L:2012:315:0001:0056:EN:PDF

  13. JIS TS B 0024–1:2010. (2010). Machine tools – test methods for electric power consumption – part 1: machining centres. Japanese Standards Association

  14. Industrial Assessment Centers (IAC). Energy Efficiency & Renewable Energy of U.S. DOE [EB/OL] <http://iac.rutgers.edu/about.php>; 2012–06–04

  15. European Commission. EUROPE (2020). a strategy for smart, sustainable and inclusive growth; 2010. http://ec.europa.eu/europe2020/index_en.htm

  16. National technical committee on energy fundamentals and management of standardization administration of China. (2013). GB/T12723-2008 General principles for establishing allowance of energy consumption per unit throughput. Beijing: Standards Press of China.

    Google Scholar 

  17. Roedger, B. J., Schoenemann, M., et al. (2020). Combining life cycle assessment and manufacturing system simulation: evaluating dynamic impacts from renewable energy supply on product-specific environmental footprints. International Journal of Precision Engineering and Manufacturing-Green Technology. https://doi.org/10.1007/s40684-020-00229-z

    Article  Google Scholar 

  18. Shi, X. (2014). Setting effective mandatory energy efficiency standards and labelling regulations: A review of best ractices in the Asia Pacific region. Applied Energy, 133, 135–143.

    Article  Google Scholar 

  19. Guo, Y., Duflou, J., Deng, Y., et al. (2018). A life cycle energy analysis integrated process planning approach to foster the sustainability of discrete part manufacturing. Energy, 153, 604–617.

    Article  Google Scholar 

  20. Reddy, B. S. (2013). Barriers and drivers to energy efficiency–A new taxonomical approach. Energy Conversion and Management, 74, 403–416.

    Article  Google Scholar 

  21. IPCC (Intergovernmental Panel on Climate Change). Contribution of Working Group III to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Chapter 5. Barriers, Opportunities, and Market Potential of Technologies and Practices. Retrieved on October 30, 2008 fromhttp://www.grida.no/publications/other/ipcc_tar/?src=/climate/ipcc_tar/wg3/index.htm

  22. Leiden, A., Herrmann, C., & Thiede, S. (2020). Cyber-physical production system approach for energy and resource efficient planning and operation of plating process chains. Journal of Cleaner Production, 280, 125160.

    Article  Google Scholar 

  23. 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 

  24. Bunse, K., Vodicka, M., Schönsleben, P., et al. (2011). Integrating energy efficiency performance in production management–gap analysis between industrial needs and scientific literature. Journal of Cleaner Production, 19(6), 667–679.

    Article  Google Scholar 

  25. de Groot, H., Verhoef, E., & Nijkamp, P. (2001). Energy saving by firms: Decision-making, barriers and policies. Energy Economics, 23(6), 717–740.

    Article  Google Scholar 

  26. Worrell, E., Bernstein, L., Roy, J., et al. (2009). Industrial energy efficiency and climate change mitigation. Energy efficiency, 2(2), 109.

    Article  Google Scholar 

  27. Lo, K. (2014). A critical review of China’s rapidly developing renewable energy and energy efficiency policies. Renewable and Sustainable Energy Reviews, 29, 508–516.

    Article  Google Scholar 

  28. Wang, Q., Zhao, Z., Zhou, P., et al. (2013). Energy efficiency and production technology heterogeneity in China: A meta-frontier DEA approach. Economic Modelling, 35, 283–289.

    Article  Google Scholar 

  29. Lin, B., & Du, K. (2013). Technology gap and China’s regional energy efficiency: A parametric metafrontier approach. Energy Economics, 40, 529–536.

    Article  Google Scholar 

  30. Lin, B., & Du, K. (2014). Measuring energy efficiency under heterogeneous technologies using a latent class stochastic frontier approach: An application to Chinese energy economy. Energy, 76, 884–890.

    Article  Google Scholar 

  31. Taylan, O., Kaya, D., & Demirbas, A. (2016). An integrated multi attribute decision model for energy efficiency processes in petrochemical industry applying fuzzy set theory. Energy Conversion and Management, 117, 501–512.

    Article  Google Scholar 

  32. Zhu, Q., Lujia, F., Mayyas, A., et al. (2015). Production energy optimization using low dynamic programming, a decision support tool for sustainable manufacturing. Journal of Cleaner Production, 105, 178–183.

    Article  Google Scholar 

  33. May, G., Barletta, I., Stahl, B., et al. (2015). Energy management in production: A novel method to develop key performance indicators for improving energy efficiency. Applied Energy, 149, 46–61.

    Article  Google Scholar 

  34. US EPA. ENERGY STAR – the power to protect the environment through energy efficiency. US Environmental Protection Agency; Washington, DC; 2003

  35. Dietmair, A., Verl, A., & Eberspaecher, P. (2011). Model based energy consumption optimisation in manufacturing system and machine control. International Journal of Manufacturing Research, 6(4), 380–401.

    Article  Google Scholar 

  36. Boyd, G., Dutrow, E., & Tunnessen, W. (2008). The evolution of the ‘“energy star”’ energy performance indicator for benchmarking industrial plant manufacturing energy use. Journal of Cleaner Production, 16(6), 709–715.

    Article  Google Scholar 

  37. Zhou, X., Liu, F., & Cai, W. (2016). An energy-consumption model for establishing energy-consumption allowance of a workpiece in a machining system. Journal of Cleaner Production, 135, 1580–1590.

    Article  Google Scholar 

  38. Cai, W., Liu, F., Xie, J., et al. (2017). A tool for assessing the energy demand and efficiency of machining systems: Energy benchmarking. Energy, 138, 332–347.

    Article  Google Scholar 

  39. Cai, W., Liu, F., Zhang, H., et al. (2017). Development of dynamic energy benchmark for mass production in machining systems for energy management and energy-efficiency improvement. Applied Energy, 202, 715–725.

    Article  Google Scholar 

  40. Park, C. W., Kwon, K. S., Kim, W. B., et al. (2009). Energy consumption reduction technology in manufacturing—A selective review of policies, standards, and research. International Journal of Precision Engineering and Manufacturing, 10(5), 151–173.

    Article  Google Scholar 

  41. Wang, Q., Liu, F., & Li, C. (2013). An integrated method for assessing the energy efficiency of machining workshop. Journal of Cleaner Production, 52, 122–133.

    Article  Google Scholar 

  42. Gao, M., Li, L., Wang, Q., et al. (2020). Energy efficiency and dynamic analysis of a novel hydraulic system with double actuator. International Journal of Precision Engineering and Manufacturing-Green Technology, 1, 1–13.

    Google Scholar 

  43. Yoon, H. S., Kim, E. S., Kim, M. S., et al. (2015). Towards greener machine tools–A review on energy saving strategies and technologies. Renewable and Sustainable Energy Reviews, 48, 870–891.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the Chongqing Research Program of Basic Research and Frontier Technology (Grant No. cstc2020jcyj-bsh0029), the Hong Kong Scholars Program (Grant No. XJ2019059), the National Natural Science Foundation of China (Grant No. 71971130, 51705055, 51875480), and the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJZD-K201903401).

Author information

Authors and Affiliations

  1. College of Engineering and Technology, Southwest University, Chongqing, 400715, China

    Wei Cai, Yuanhui Zhang & Li Li

  2. Faculty of Business, The Hong Kong Polytechnic University, Hung Hum, Kowloon, Hong Kong, China

    Wei Cai

  3. College of Mechanical Engineering, Chongqing Technology and Business University, Chongqing, 400050, China

    Jun Xie

  4. Department of Industrial Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Shun Jia

  5. School of Electrical Engineering, Chongqing Vocational Institute of Engineering, Chongqing, 402260, China

    Shaohua Hu

  6. Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, Zhejiang University, Hangzhou, 310027, China

    Luoke Hu

Authors
  1. Wei Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuanhui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Li Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shun Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shaohua Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Luoke Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luoke Hu.

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

Cai, W., Zhang, Y., Xie, J. et al. Energy Performance Evaluation Method for Machining Systems Towards Energy Saving and Emission Reduction. Int. J. of Precis. Eng. and Manuf.-Green Tech. 9, 633–644 (2022). https://doi.org/10.1007/s40684-021-00365-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40684-021-00365-0

Keywords

Search

Navigation