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Cutting energy consumption modelling for prismatic machining features

  • Lihui Wang
  • Yue Meng
  • Wei Ji
  • Xianli LiuEmail author
ORIGINAL ARTICLE
  • 15 Downloads

Abstract

Targeting energy-efficient machining process planning, this paper presents a follow-up research on cutting energy consumption modelling for prismatic machining features (PMFs). Based on the investigation of plastic deformation-based energy consumption, its energy consumption model is extended to PMFs by refining machining time and feed at corners. Material removal volume associated with machining strategies for the PMF machining is considered as well. Moreover, cutting energy consumption models are established for the selected PMFs, i.e. face, step, slot and pocket. Finally, energy consumptions in machining of a designed test part, involving the established models of cutting energy consumption for the selected PMFs, are measured and compared with estimated energy consumptions to validate the developed models.

Keywords

Cutting energy consumption Machining Prismatic machining features 

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Notes

Acknowledgements

The authors sincerely thank all the anonymous reviewers for their valuable suggestions on the improvement of our paper.

Funding information

This work is partially supported by the National Natural Science Foundation of China (Grant No.51720105009).

References

  1. 1.
    Dahmus JB, Gutowski TG (2004) An environmental analysis of machining. In: ASME 2004 Int. Mech. Eng. Congr. Expo. pp 643–652Google Scholar
  2. 2.
    Behrendt T, Zein A, Min S (2012) Development of an energy consumption monitoring procedure for machine tools. CIRP Ann - Manuf Technol 61:43–46.  https://doi.org/10.1016/j.cirp.2012.03.103 CrossRefGoogle Scholar
  3. 3.
    Munoz AA, Sheng P (1995) An analytical approach for determining the environmental impact of machining processes. J Mater Process Technol 53:736–758.  https://doi.org/10.1016/0924-0136(94)01764-R CrossRefGoogle Scholar
  4. 4.
    Altintas Y, Yellowley I (1989) In-process detection of tool failure in milling using cutting force models. J Eng Ind 111:149.  https://doi.org/10.1115/1.3188744 CrossRefGoogle Scholar
  5. 5.
    Shao H, Wang HL, Zhao XM (2004) A cutting power model for tool wear monitoring in milling. Int J Mach Tools Manuf 44:1503–1509.  https://doi.org/10.1016/j.ijmachtools.2004.05.003 CrossRefGoogle Scholar
  6. 6.
    Waldorf DJ, Kapoor SG, DeVor RE (1992) Automatic recognition of tool wear on a face mill using a mechanistic modeling approach. Wear 157:305–323.  https://doi.org/10.1016/0043-1648(92)90069-K CrossRefGoogle Scholar
  7. 7.
    Wang L, Wang W, Liu D (2017) Dynamic feature based adaptive process planning for energy-efficient NC machining. CIRP Ann - Manuf Technol 66:1–4.  https://doi.org/10.1016/j.cirp.2017.04.015 CrossRefGoogle Scholar
  8. 8.
    Grzesik W, Denkena B, Żak K, Grove T, Bergmann B (2016) Energy consumption characterization in precision hard machining using CBN cutting tools. Int J Adv Manuf Technol 85:2839–2845.  https://doi.org/10.1007/s00170-015-8091-1 CrossRefGoogle Scholar
  9. 9.
    Xu K, Tang K (2016) An energy saving approach for rough milling tool path planning. Comput Aided Des Appl 13:253–264.  https://doi.org/10.1080/16864360.2015.1084198 CrossRefGoogle Scholar
  10. 10.
    Li W, Kara S (2011) An empirical model for predicting energy consumption of manufacturing processes: a case of turning process. Proc Inst Mech Eng Part B J Eng Manuf 225:1636–1646.  https://doi.org/10.1177/2041297511398541 CrossRefGoogle Scholar
  11. 11.
    Liu D, Wang W, Wang L (2017) Energy-efficient cutting parameters determination for NC machining with specified machining accuracy. Procedia CIRP 61:523–528.  https://doi.org/10.1016/j.procir.2016.11.215 CrossRefGoogle Scholar
  12. 12.
    Liu N, Zhang YF, Lu WF (2015) A hybrid approach to energy consumption modelling based on cutting power : a milling case. J Clean Prod 104:264–272.  https://doi.org/10.1016/j.jclepro.2015.05.049 CrossRefGoogle Scholar
  13. 13.
    Ma J, Ge X, Chang SI, Lei S (2014) Assessment of cutting energy consumption and energy efficiency in machining of 4140 steel. Int J Adv Manuf Technol 74:1701–1708.  https://doi.org/10.1007/s00170-014-6101-3 CrossRefGoogle Scholar
  14. 14.
    Gutowski TG, Sekulic DP (2011) Thermodynamic analysis of resources used in manufacturing processes 1. Environ Sci Technol:1–43Google Scholar
  15. 15.
    Kara S, Li W (2011) Unit process energy consumption models for material removal processes. CIRP Ann - Manuf Technol 60:37–40.  https://doi.org/10.1016/j.cirp.2011.03.018 CrossRefGoogle Scholar
  16. 16.
    Quintana G, Ciurana J, Ribatallada J (2011) Modelling power consumption in ball-end milling operations. Mater Manuf Process 26:746–756.  https://doi.org/10.1080/10426910903536824 CrossRefGoogle Scholar
  17. 17.
    Diaz N, Redelsheimer E, Dornfeld D (2011) Energy consumption characterization and reduction strategies for milling machine tool use. In: Glocalized Solut. Sustain. Manuf. Springer, Berlin, Heidelb. pp 263–267Google Scholar
  18. 18.
    Jia S, Tang R, Lv J (2014) Therblig-based energy demand modeling methodology of machining process to support intelligent manufacturing. J Intell Manuf 25:913–931.  https://doi.org/10.1007/s10845-012-0723-9 CrossRefGoogle Scholar
  19. 19.
    Fang K, Uhan N, Zhao F, Sutherland JW (2011) A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction. J Manuf Syst 30:234–240.  https://doi.org/10.1016/j.jmsy.2011.08.004 CrossRefGoogle Scholar
  20. 20.
    Hu L, Liu Y, Peng C, Tang W, Tang R, Tiwari A (2018) Minimising the energy consumption of tool change and tool path of machining by sequencing the features. Energy 147:390–402.  https://doi.org/10.1016/j.energy.2018.01.046 CrossRefGoogle Scholar
  21. 21.
    Hu L, Liu Y, Peng C, Tang W, Tang R, Tiwari A (2017) Minimising the machining energy consumption of a machine tool by sequencing the features of a part. Energy 121:292–305.  https://doi.org/10.1016/j.energy.2017.01.039 CrossRefGoogle Scholar
  22. 22.
    Mativenga PT, Rajemi MF (2011) Calculation of optimum cutting parameters based on minimum energy footprint. CIRP Ann - Manuf Technol 60:149–152.  https://doi.org/10.1016/j.cirp.2011.03.088 CrossRefGoogle Scholar
  23. 23.
    Rajemi MF, Mativenga PT, Aramcharoen A (2010) Sustainable machining: selection of optimum turning conditions based on minimum energy considerations. J Clean Prod 18:1059–1065.  https://doi.org/10.1016/j.jclepro.2010.01.025 CrossRefGoogle Scholar
  24. 24.
    Santos JP, Oliveira M, Almeida FG, Pereira JP, Reis A (2011) Improving the environmental performance of machine-tools: influence of technology and throughput on the electrical energy consumption of a press-brake. J Clean Prod 19:356–364.  https://doi.org/10.1016/j.jclepro.2010.10.009 CrossRefGoogle Scholar
  25. 25.
    Camposeco-Negrete C (2013) Optimization of cutting parameters for minimizing energy consumption in turning of AISI 6061 T6 using Taguchi methodology and ANOVA. J Clean Prod 53:195–203.  https://doi.org/10.1016/j.jclepro.2013.03.049 CrossRefGoogle Scholar
  26. 26.
    Bilga PS, Singh S, Kumar R (2016) Optimization of energy consumption response parameters for turning operation using Taguchi method. J Clean Prod 137:1406–1417.  https://doi.org/10.1016/j.jclepro.2016.07.220 CrossRefGoogle Scholar
  27. 27.
    Wang B, Liu Z, Song Q, Wan Y, Shi Z (2016) Proper selection of cutting parameters and cutting tool angle to lower the specific cutting energy during high speed machining of 7050-T7451 aluminum alloy. J Clean Prod 129:292–304.  https://doi.org/10.1016/j.jclepro.2016.04.071 CrossRefGoogle Scholar
  28. 28.
    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. J Clean Prod 66:309–316.  https://doi.org/10.1016/j.jclepro.2013.10.025 CrossRefGoogle Scholar
  29. 29.
    Camposeco-negrete C (2015) Optimization of cutting parameters using response surface method for minimizing energy consumption and maximizing cutting quality in turning of AISI 6061 T6 aluminum. J Clean Prod 91:109–117.  https://doi.org/10.1016/j.jclepro.2014.12.017 CrossRefGoogle Scholar
  30. 30.
    Ma Y, Feng P, Zhang J, Wu Z, Yu D (2015) Energy criteria for machining-induced residual stresses in face milling and their relation with cutting power. Int J Adv Manuf Technol 81:1023–1032.  https://doi.org/10.1007/s00170-015-7278-9 CrossRefGoogle Scholar
  31. 31.
    Li C, Xiao Q, Tang Y, Li L (2016) A method integrating Taguchi , RSM and MOPSO to CNC machining parameters optimization for energy saving. 135:263–275. doi:  https://doi.org/10.1016/j.jclepro.2016.06.097
  32. 32.
    Peng T, Xu X, Wang L (2013) A novel energy demand modelling approach for CNC machining based on function blocks. J Manuf Syst 33:196–208.  https://doi.org/10.1016/j.jmsy.2013.12.004 CrossRefGoogle Scholar
  33. 33.
    Bi ZM, Wang L (2012) Energy modeling of machine tools for optimization of machine setups. IEEE Trans Autom Sci Eng 9:607–613CrossRefGoogle Scholar
  34. 34.
    Meng Y, Wang L, Lee C-H, Ji W, Liu X (2018) Plastic deformation-based energy consumption modelling for machining. Int J Adv Manuf Technol 96:631–641.  https://doi.org/10.1007/s00170-017-1521-5 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Mechanical and Power EngineeringHarbin University of Science and TechnologyHarbinChina
  2. 2.Department of Production EngineeringKTH Royal Institute of TechnologyStockholmSweden

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