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Cutting energy consumption modelling for prismatic machining features

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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.

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References

  1. Dahmus JB, Gutowski TG (2004) An environmental analysis of machining. In: ASME 2004 Int. Mech. Eng. Congr. Expo. pp 643–652

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  14. Gutowski TG, Sekulic DP (2011) Thermodynamic analysis of resources used in manufacturing processes 1. Environ Sci Technol:1–43

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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 263–267

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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. 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

    Article  Google Scholar 

  33. Bi ZM, Wang L (2012) Energy modeling of machine tools for optimization of machine setups. IEEE Trans Autom Sci Eng 9:607–613

    Article  Google Scholar 

  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

    Article  Google Scholar 

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Acknowledgements

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

Funding

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

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Authors and Affiliations

  1. School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, 150080, China

    Lihui Wang, Yue Meng, Wei Ji & Xianli Liu

  2. Department of Production Engineering, KTH Royal Institute of Technology, 100 44, Stockholm, Sweden

    Lihui Wang & Wei Ji

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  1. Lihui Wang
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  2. Yue Meng
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  4. Xianli Liu
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Correspondence to Xianli Liu.

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Wang, L., Meng, Y., Ji, W. et al. Cutting energy consumption modelling for prismatic machining features. Int J Adv Manuf Technol 103, 1657–1667 (2019). https://doi.org/10.1007/s00170-019-03667-5

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  • DOI: https://doi.org/10.1007/s00170-019-03667-5

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