Advertisement

Optimal Cutting Parameters to Reduce Power Consumption in Face Milling of a Cast Iron Alloy for Environmental Sustainability

  • Xiaona Luan
  • Song ZhangEmail author
  • Gaoli Cai
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 52)

Abstract

In the perspective of energy saving, the power consumption in the process of CNC (Computer numerical control) machining is closely related to the environmental issues. Therefore, it is especially important to optimize the cutting parameters to reduce the power consumption. In this paper, the power consumption which is determined by the cutting parameters in the face milling process of a cast iron alloy is researched. First, characteristics of machine tool power consumption were studied and the relationship between power consumption and cutting forces was described qualitatively. Secondly, a power consumption monitoring system was built to monitor and record the power consumption in real time during a face milling process. Secondly, according to central composite design (CCD), a total of 27 experiments were carried out to reveal the relationship between the power consumption and process parameters. Finally, the milling parameters were optimized by means of response surface methodology (RSM). The results indicate that the power consumption of P M and P Y can be saved by 38.55 and 28.23 % under the cutting condition of optimized parameters, and the surface quality is insured simultaneously.

Keywords

Power consumption Optimization Cutting parameters A cast iron alloy Face milling 

Notes

Acknowledgements

This work is supported by National Major Science and Technology Project: High-end CNC Machine Tools and Basic Manufacturing Equipments (Grant No. 2015ZX04003-005).

References

  1. 1.
    Park, Y.J., Lee, G.B.: Analysis of energy efficiency and productivity in dry process in PCB manufacturing. Int. J. Preci. Engin. Manuf. 14(7), 1213–1221 (2013)CrossRefGoogle Scholar
  2. 2.
    IEA 2015: World Energy Outlook 2015. http://www.worldenergyoutlook.org/
  3. 3.
    de Lacalle, N.L.: Aitzol Lamikiz Mentxaka, Machine Tools for High Performance Machining, pp. 85–108. Springer Science & Business Media, Berlin (2008)Google Scholar
  4. 4.
    Hanafi, I., Khamlichi, A., Cabrera, F.M., Almansa, E., Jabbouri., A.: Optimization of cutting conditions for sustainable machining of PEEK-CF30 using TiN tools. J. Clean. Prod. 33, 1–9 (2013)Google Scholar
  5. 5.
    Diaz, N., Choi, S., Helu, M., Chen, Y., Jayanathan, S., Yasui, Y.: Machine tool design and operation strategies for green manufacturing. Procedia CIRP 14, 612–620 (2010)Google Scholar
  6. 6.
    Dahmus, J.B., Gutowski, T.G.: An environmental analysis of machining. In: Proceedings of IMECE2004 ASME International Mechanical Engineering Congress and RD&D Expo-Anaheim, pp. 13–19 (2004)Google Scholar
  7. 7.
    Zolgharni, M., Jones, B.J., Bulpett, R., Anson, A.W., Franks, J.: Energy efficiency improvements in dry drilling with optimized diamond-like carbon coatings. Diam. Relat. Mater. 17(7), 1733–1737 (2008)CrossRefGoogle Scholar
  8. 8.
    Yingjie, Z.: Energy efficiency techniques in machining process: a review. Int. J. Adv. Manuf. Technol. 71(5–8), 1123–1132 (2014)CrossRefGoogle Scholar
  9. 9.
    Bi, Z.M., Wang, L.: Optimization of machining processes from the perspective of energy consumption: A case study. J. Manufact. Syst. 31(4), 420–428 (2012)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Lv, J., Tang, R., Jia, S.: Therblig-based energy supply modeling of computer numerical control machine tools. J. Clean. Prod. 65, 168–177 (2014)CrossRefGoogle Scholar
  11. 11.
    Mesquita, R., Krasteva, E., Doytchinov, S.: Computer-aided selection of optimum machining parameters in multipass turning. Int. J. Adv. Manuf. Technol. 10(1), 19–26 (1995)CrossRefGoogle Scholar
  12. 12.
    Fuad, M.M.M.: Multi-objective Optimization for Clustering Microarray Gene Expression Data-A Comparative Study, Agent and Multi-Agent Systems: Technologies and Applications. Springer International Publishing, Berlin, pp. 123–133 (2015)Google Scholar
  13. 13.
    Bhushan, R.K.: Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites. J. Clean. Prod. 39, 242–254 (2013)CrossRefGoogle Scholar
  14. 14.
    Zhang, S., Li, J.F., Sun, J.: Tool wear and cutting forces variation in high-speed end-milling Ti-6Al-4 V alloy. Int. J. Adv. Manuf. Technol. 46(1–4), 69–78 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Key Laboratory of High Efficiency and Clean Mechanical Manufacture (Ministry of Education); School of Mechanical EngineeringShandong UniversityJinanChina

Personalised recommendations