Skip to main content

Advertisement

SpringerLink
Log in

Experimental and theoretical investigation of milling tool selection towards energy-efficient process planning in discrete parts manufacturing

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Recently, energy-efficient process planning has aroused numerous concerns from both industry and academia. However, as one of the most important tasks of process planning in discrete parts manufacturing system, tool selection has seldom been investigated in terms of energy efficiency. Without a good understanding of its effects on energy efficiency of machining process, energy-efficient manufacturing cannot be achieved to a satisfactory level. To bridge this research gap, this paper studies tool selection effects by analysing tools with different geometrical parameters, i.e. cutter radius, flute number and helix angle. Their effects on energy efficiency of slot milling process are analysed using experimental approach and theoretical approach, respectively. In experimental approach, Taguchi method is firstly applied to study the effects of each tool geometrical parameter. Then, analysis of variance (ANOVA) is performed to study the significance of each factor. In theoretical approach, the influence of tool selection on cutting power is analytically revealed. Subsequently, the energy efficiency of different cutters is quantitatively analysed through numerical experiments. The results are quite promising with both approaches leading to the same conclusion. The rank of influence is cutter radius > flute number > helix angle. Both methods suggest that a milling cutter with a larger cutter radius and fewer flutes will improve the energy efficiency in milling process. Although there is a small discrepancy in helix angle, it does not affect the good agreement of these two methods since the influence of the helix angle is nearly negligible. Compared to experimental approach, theoretical approach may have more application potential since it may help reduce time and waste of materials. To the best knowledge of the authors, the effects of tool selection on energy efficiency in milling process are systematically analysed for the first time, which significantly advances the state of the art in energy-efficient process planning.

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

Similar content being viewed by others

References

  1. Liu N, Zhang YF, Lu WF (2019) Improving energy efficiency in discrete parts manufacturing system using an ultra-flexible job shop scheduling algorithm. Int J Precis Eng Manuf Technol 6:349–365. https://doi.org/10.1007/s40684-019-00055-y

    Article  Google Scholar 

  2. Mori M, Fujishima M, Inamasu Y, Oda Y (2011) A study on energy efficiency improvement for machine tools. CIRP Ann - Manuf Technol 60:145–148. https://doi.org/10.1016/j.cirp.2011.03.099

    Article  Google Scholar 

  3. He Y, Liu F, Wu T, Zhong FP, Peng B (2012) Analysis and estimation of energy consumption for numerical control machining. Proc Inst Mech Eng Part B J Eng Manuf 226:255–266. https://doi.org/10.1177/0954405411417673

    Article  Google Scholar 

  4. Altıntaş RS, Kahya M, Ünver HÖ (2016) Modelling and optimization of energy consumption for feature based milling. Int J Adv Manuf Technol 86:3345–3363. https://doi.org/10.1007/s00170-016-8441-7

    Article  Google Scholar 

  5. Wang Z (2017) Optimization calculation of reverse energy consumption based on feature parameter of NC code. Int J Adv Manuf Technol 93:3437–3448. https://doi.org/10.1007/s00170-017-0746-7

    Article  Google Scholar 

  6. Chen X, Li C, Jin Y, Li L (2018) Optimization of cutting parameters with a sustainable consideration of electrical energy and embodied energy of materials. Int J Adv Manuf Technol 96:775–788. https://doi.org/10.1007/s00170-018-1647-0

    Article  Google Scholar 

  7. Gutowski T, Dahmus J, Thiriez A (2006) Electrical energy requirements for manufacturing processes. In: 13th CIRP Int Conf life cycle Eng, LCE 2006, pp 623–627

  8. 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 

  9. Nancy D, David D (2011) Energy consumption characterization and reduction strategies for milling machine tool use. In: 18th CIRP International Conference on Life Cycle Engineering, LCE 2011, pp 263–267

  10. 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 

  11. Li W, Winter M, Kara S, Herrmann C (2012) Eco-efficiency of manufacturing processes: a grinding case. CIRP Ann - Manuf Technol 61:59–62. https://doi.org/10.1016/j.cirp.2012.03.029

    Article  Google Scholar 

  12. Li L, Yan J, Xing Z (2013) Energy requirements evaluation of milling machines based on thermal equilibrium and empirical modelling. J Clean Prod 52:113–121. https://doi.org/10.1016/j.jclepro.2013.02.039

    Article  Google Scholar 

  13. 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 

  14. Shi KN, Zhang DH, Liu N, Wang SB, Ren JX, Wang SL (2018) A novel energy consumption model for milling process considering tool wear progression. J Clean Prod 184:152–159. https://doi.org/10.1016/j.jclepro.2018.02.239

    Article  Google Scholar 

  15. Hanafi I, Khamlichi A, Cabrera FM, Almansa E, Jabbouri A (2012) Optimization of cutting conditions for sustainable machining of PEEK-CF30 using TiN tools. J Clean Prod 33:1–9. https://doi.org/10.1016/j.jclepro.2012.05.005

    Article  Google Scholar 

  16. 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 

  17. Bhushan RK (2013) 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. https://doi.org/10.1016/j.jclepro.2012.08.008

    Article  Google Scholar 

  18. 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 

  19. Zhang Y, Zou P, Li B, Liang S (2015) Study on optimized principles of process parameters for environmentally friendly machining austenitic stainless steel with high efficiency and little energy consumption. Int J Adv Manuf Technol 79:89–99. https://doi.org/10.1007/s00170-014-6763-x

    Article  Google Scholar 

  20. Camposeco-Negrete C, Nájera J de DC, Miranda-Valenzuela JC (2016) Optimization of cutting parameters to minimize energy consumption during turning of AISI 1018 steel at constant material removal rate using robust design. Int J Adv Manuf Technol 83(5–8):1341–1347. https://doi.org/10.1007/s00170-015-7679-9

    Article  Google Scholar 

  21. Li C, Xiao Q, Tang Y, Li L (2016) A method integrating Taguchi, RSM and MOPSO to CNC machining parameters optimization for energy saving. J Clean Prod 135:263–275. https://doi.org/10.1016/j.jclepro.2016.06.097

    Article  Google Scholar 

  22. Ma F, Zhang H, Cao H, Hon KKB (2017) An energy consumption optimization strategy for CNC milling. Int J Adv Manuf Technol 90:1715–1726. https://doi.org/10.1007/s00170-016-9497-0

    Article  Google Scholar 

  23. Shin SJ, Woo J, Rachuri S (2017) Energy efficiency of milling machining: component modeling and online optimization of cutting parameters. J Clean Prod 161:12–29. https://doi.org/10.1016/j.jclepro.2017.05.013

    Article  Google Scholar 

  24. Li C, Chen X, Tang Y, Li L (2017) Selection of optimum parameters in multi-pass face milling for maximum energy efficiency and minimum production cost. J Clean Prod 140:1805–1818. https://doi.org/10.1016/j.jclepro.2016.07.086

    Article  Google Scholar 

  25. Warsi SS, Jaffery SHI, Ahmad Khan RM, Agha MH, Ali L (2018) Development and analysis of energy consumption map for high-speed machining of Al 6061-T6 alloy. Int J Adv Manuf Technol 96:91–102. https://doi.org/10.1007/s00170-018-1588-7

    Article  Google Scholar 

  26. Li B, Cao H, Yan J, Jafar S (2017) A life cycle approach to characterizing carbon efficiency of cutting tools. Int J Adv Manuf Technol 93:3347–3355. https://doi.org/10.1007/s00170-017-0728-9

    Article  Google Scholar 

  27. Wang YC, Kim DW, Katayama H, Hsueh WC (2018) Optimization of machining economics and energy consumption in face milling operations. Int J Adv Manuf Technol 99:1–8. https://doi.org/10.1007/s00170-018-1848-6

    Article  Google Scholar 

  28. Ma GH, Zhang YF, Nee a YC (2000) A simulated annealing-based optimization algorithm for process planning. Int J Prod Res 38:2671–2687. https://doi.org/10.1080/002075400411420

    Article  Google Scholar 

  29. Ozturk E, Ozkirimli O, Gibbons T, Saibi M, Turner S (2016) Prediction of effect of helix angle on cutting force coefficients for design of new tools. CIRP Ann - Manuf Technol 65:125–128. https://doi.org/10.1016/j.cirp.2016.04.042

    Article  Google Scholar 

Download references

Funding

This study was supported by the National Natural Science Foundation of China (Grant No. 51775444 and Grant No. 41601117). The authors would like to acknowledge this financial support.

Author information

Author notes

  1. K. N. Shi, N. Liu and S. B. Wang contributed equally to this work.

Authors and Affiliations

  1. The Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University (NPU), Xi’an, Shaanxi, 710072, People’s Republic of China

    K. N. Shi & J. X. Ren

  2. Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117576, Singapore

    N. Liu

  3. State Key Laboratory of Mechanical Transmission, College of Mechanical Engineering, Chongqing University, Chongqing, 400044, People’s Republic of China

    S. B. Wang

  4. School of humanities, economics and laws, Northwestern Polytechnical University (NPU), Xi’an, Shaanxi, 710072, People’s Republic of China

    Y. Yuan

Authors
  1. K. N. Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. B. Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J. X. Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Y. Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to N. Liu.

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

Shi, K.N., Liu, N., Wang, S.B. et al. Experimental and theoretical investigation of milling tool selection towards energy-efficient process planning in discrete parts manufacturing. Int J Adv Manuf Technol 104, 1099–1107 (2019). https://doi.org/10.1007/s00170-019-03960-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-019-03960-3

Keywords

Search

Navigation