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Energy consumption modelling in milling of variable curved geometry

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Abstract

The accurate estimation of energy consumption is beneficial to manufacturing enterprises economically as well as to overcome global energy crisis. The present work concentrates on developing an energy consumption model in milling of variable curved geometries where magnitudes and directions of workpiece curvature vary along tool contact path of a component. The current work deals with estimation and analysis of energy consumption in peripheral milling of variable curved surfaces where cutting forces differ along tool contact path in the presence of workpiece curvature. The proposed hybrid model developed in MATLAB involves process mechanics, cutting forces and energy consumption and has modules for idle, auxiliary and cutting power. The proposed model is validated by the experimental work. The model is generic and versatile in nature and is useful for milling of straight, circular and curved surfaces. In addition to it, the influence of workpiece curvature on power consumption has been investigated to realize the variation of power consumption along the tool contact path. The developed model offers a basic platform to understand and characterize the energy consumption for general peripheral milling considering workpiece geometry. The comparison of predicted and measured results indicates that the model is capable to estimate the power consumption accurately. The proposed model will be used by the practitioners to find the optimum cutting conditions to reduce power consumption during the machining of curved geometries – a pragmatic condition but not much researched condition in machining.

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Abbreviations

\(P_{{{\text{idle}}}}\) :

Idle power [W]

\(\overline{P}_{{{\text{cutting}}}}\) :

Cutting power [W]

\(P_{{{\text{auxiliary}}}}\) :

Auxiliary power [W]

\(P_{{{\text{total}}}}\) :

Total power [W]

X t (u), Y t (u) :

Parametric curve of locus of tool centre

X wb (u), Y wb (u) :

Parametric curve of before cut workpiece trajectory

X wa (u), Y wa (u) :

Parametric curve of after cut workpiece trajectory

X′(u), Y′(u) :

Derivative of parametric w.r. parameter

d r :

Offset distance between before cut and after cut workpiece trajectory [mm]

r :

Milling cutter radius [mm]

f cc :

Feed per tooth along tool contact path [mm]

θ en :

Entry angle [radian]

θ ex :

Exit angle [radian]

R :

Radius of curvature [mm]

t c :

Uncut chip thickness

a e :

Radial immersion [mm]

a p :

Axial immersion [mm]

\(\alpha\) :

Helix angle [radian]

K t,  K r :

Cutting constants

\(dF_{i,j,f} (\varphi )\) :

Feed force acting on tooth j at angular rotation \(\varphi\)

\(dF_{i,j,n} (\varphi )\) :

Normal force acting on tooth j at angular rotation \(\varphi\)

\(P_{{{\text{cutting}}}}\) :

Instantaneous cutting power [W

References

  1. International Energy Agency (IEA) (2016) World energy outlook special report 2016: energy and air pollution. https://www.iea.org/reports/energy-and-air-pollution

  2. Peng T, Xu X (2014) Energy-efficient machining systems: a critical review. Int J Adv Manuf Technol 72:1389–1406. https://doi.org/10.1007/s00170-014-5756-0

    Article  Google Scholar 

  3. Sihag N, Sangwan KS (2020) A systematic literature review on machine tool energy consumption. J Clean Prod 275:123125. https://doi.org/10.1016/j.jclepro.2020.123125

    Article  Google Scholar 

  4. Mourtzis D (2020) Simulation in the design and operation of manufacturing systems: state of the art and new trends. Int J Prod Res 58:1927–1949. https://doi.org/10.1080/00207543.2019.1636321

    Article  Google Scholar 

  5. Mourtzis D, Vlachou E, Milas N et al (2019) A cloud-based, knowledge-enriched framework for increasing machining efficiency based on machine tool monitoring. Proc Inst Mech Eng Part B J Eng Manuf 233:278–292. https://doi.org/10.1177/0954405417716727

    Article  Google Scholar 

  6. Dietmair A, Verl A (2009) A generic energy consumption model for decision making and energy efficiency optimisation in manufacturing. Int J Sustain Eng 2:123–133. https://doi.org/10.1080/19397030902947041

    Article  Google Scholar 

  7. Avram OI, Xirouchakis P (2011) Evaluating the use phase energy requirements of a machine tool system. J Clean Prod 19:699–711. https://doi.org/10.1016/j.jclepro.2010.10.010

    Article  Google Scholar 

  8. 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 

  9. He Y, Liu F, Wu T et al (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 

  10. Balogun VA, Mativenga PT (2013) Modelling of direct energy requirements in mechanical machining processes. J Clean Prod 41:179–186. https://doi.org/10.1016/j.jclepro.2012.10.015

    Article  Google Scholar 

  11. Moradnazhad M, Unver HO (2017) Energy consumption characteristics of turn-mill machining. Int J Adv Manuf Technol 91:1991–2016. https://doi.org/10.1007/s00170-016-9868-6

    Article  Google Scholar 

  12. 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 

  13. Edem IF, Mativenga PT (2017) Modelling of energy demand from computer numerical control (CNC) toolpaths. J Clean Prod 157:310–321. https://doi.org/10.1016/j.jclepro.2017.04.096

    Article  Google Scholar 

  14. Gu W, Li Z, Chen Z, Li Y (2020) An energy-consumption model for establishing an integrated energy-consumption process in a machining system. Math Comput Model Dyn Syst 26:534–561. https://doi.org/10.1080/13873954.2020.1833045

    Article  Google Scholar 

  15. Yu S, Zhao G, Li C et al (2021) Prediction models for energy consumption and surface quality in stainless steel milling. Int J Adv Manuf Technol 117:3777–3792. https://doi.org/10.1007/s00170-021-07971-x

    Article  Google Scholar 

  16. Gutowski T, Dahmus J, Thiriez A (2006) Electrical energy requirements for manufacturing processes. Proc 13th CIRP Int Conf Life Cycle Eng LCE 2006 623–628

  17. 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 

  18. 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 

  19. Aramcharoen A, Mativenga PT (2014) Critical factors in energy demand modelling for CNC milling and impact of toolpath strategy. J Clean Prod 78:63–74. https://doi.org/10.1016/j.jclepro.2014.04.065

    Article  Google Scholar 

  20. Zhou L, Li J, Li F et al (2017) An improved cutting power model of machine tools in milling process. Int J Adv Manuf Technol 91:2383–2400. https://doi.org/10.1007/s00170-016-9929-x

    Article  Google Scholar 

  21. Nguyen TT (2019) Prediction and optimization of machining energy, surface roughness, and production rate in SKD61 milling. Meas J Int Meas Confed 136:525–544. https://doi.org/10.1016/j.measurement.2019.01.009

    Article  Google Scholar 

  22. Yuan J, Shao H, Cai Y, Shi X (2021) Energy efficiency state identification of milling processing based on EEMD-PCA-ICA. Meas J Int Meas Confed 174:109014. https://doi.org/10.1016/j.measurement.2021.109014

    Article  Google Scholar 

  23. Yoon HS, Lee JY, Kim MS, Ahn SH (2014) Empirical power-consumption model for material removal in three-axis milling. J Clean Prod 78:54–62. https://doi.org/10.1016/j.jclepro.2014.03.061

    Article  Google Scholar 

  24. Sealy MP, Liu ZY, Zhang D et al (2016) Energy consumption and modeling in precision hard milling. J Clean Prod 135:1591–1601. https://doi.org/10.1016/j.jclepro.2015.10.094

    Article  Google Scholar 

  25. Xie J, Liu F, Huang J, Qiu H (2016) Mapping acquisition of loading loss coefficient of main driving system of machine tools. Proc Inst Mech Eng Part B J Eng Manuf 230:1264–1271. https://doi.org/10.1177/0954405415623488

    Article  Google Scholar 

  26. Lv J, Tang R, Jia S, Liu Y (2016) Experimental study on energy consumption of computer numerical control machine tools. J Clean Prod 112:3864–3874. https://doi.org/10.1016/j.jclepro.2015.07.040

    Article  Google Scholar 

  27. Zhang C, Zhou Z, Tian G et al (2018) Energy consumption modeling and prediction of the milling process: a multistage perspective. Proc Inst Mech Eng Part B J Eng Manuf 232:1973–1985. https://doi.org/10.1177/0954405416682278

    Article  Google Scholar 

  28. Wang SM, Lee CY, Gunawan H, Yeh CC (2019) An accuracy-efficiency-power consumption hybrid optimization method for CNC milling process. Appl Sci 9. https://doi.org/10.3390/APP9071495

    Article  Google Scholar 

  29. Wang Y, Li L, Lingling L, Cai W (2018) Exploring the effect of un-deformed chip parameters on energy consumption for energy efficiency improvement in the milling. Procedia CIRP 72:1380–1385. https://doi.org/10.1016/j.procir.2018.03.075

    Article  Google Scholar 

  30. Zhao G, Guo YB, Zhu P, Zhao Y (2018) Energy consumption characteristics and influence on surface quality in milling. Procedia CIRP 71:111–115. https://doi.org/10.1016/j.procir.2018.05.081

    Article  Google Scholar 

  31. Tlhabadira I, Daniyan IA, Masu L, Mpofu K (2021) Development of a model for the optimization of energy consumption during the milling operation of titanium alloy (Ti6Al4V). Mater Today Proc 38:614–620. https://doi.org/10.1016/j.matpr.2020.03.477

    Article  Google Scholar 

  32. 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 

  33. Xie J, Liu F, Qiu H (2016) An integrated model for predicting the specific energy consumption of manufacturing processes. Int J Adv Manuf Technol 85:1339–1346. https://doi.org/10.1007/s00170-015-8033-y

    Article  Google Scholar 

  34. Shi KN, Zhang DH, Liu N et al (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 

  35. Shi KN, Ren JX, Wang SB et al (2019) An improved cutting power-based model for evaluating total energy consumption in general end milling process. J Clean Prod 231:1330–1341. https://doi.org/10.1016/j.jclepro.2019.05.323

    Article  Google Scholar 

  36. Yang D, Liu Y, Xie F, Xiao X (2019) Analytical investigation of workpiece internal energy generation in peripheral milling of titanium alloy Ti–6Al–4V. Int J Mech Sci 161–162. https://doi.org/10.1016/j.ijmecsci.2019.105063

    Article  Google Scholar 

  37. Wang Q, Zhang D, Chen B et al (2019) Energy consumption model for drilling processes based on cutting force. Appl Sci 9. https://doi.org/10.3390/app9224801

    Article  Google Scholar 

  38. Wang Q, Zhang D, Tang K, Zhang Y (2019) Energy consumption model for milling processes considering auxiliary load loss and its applications. Int J Adv Manuf Technol 105:4309–4323. https://doi.org/10.1007/s00170-019-04479-3

    Article  Google Scholar 

  39. Rao KV (2019) Power consumption optimization strategy in micro ball-end milling of D2 steel via TLBO coupled with 3D FEM simulation. Meas J Int Meas Confed 132:68–78. https://doi.org/10.1016/j.measurement.2018.09.044

    Article  Google Scholar 

  40. Zhang X, Yu T, Dai Y et al (2020) Energy consumption considering tool wear and optimization of cutting parameters in micro milling process. Int J Mech Sci 178:105628. https://doi.org/10.1016/j.ijmecsci.2020.105628

    Article  Google Scholar 

  41. Pawar SS, Bera TC, Sangwan KS (2021) Modelling of energy consumption for milling of circular geometry. Procedia CIRP 98:470–475. https://doi.org/10.1016/j.procir.2018.02.026

    Article  Google Scholar 

  42. Bera TC (2011) Studies on tool/workpiece deflections in peripheral milling of tubular geometries, PhD thesis, Indian Institute of Technology, Delhi

  43. Kline WA, DeVor RE, Shareef IA (1982) Prediction of surface accuracy in end milling. J Eng Ind 104:272–278. https://doi.org/10.1115/1.3185830

    Article  Google Scholar 

  44. Rao VS, Rao PVM (2006) Effect of workpiece curvature on cutting forces and surface error in peripheral milling. Proc Inst Mech Eng Part B J Eng Manuf 220:1399–1407. https://doi.org/10.1243/09544054JEM397

    Article  Google Scholar 

  45. Hu S, Liu F, He Y, Peng B (2010) Characteristics of additional load losses of spindle system of machine tools. J Adv Mech Des Syst Manuf 4:1221–1233. https://doi.org/10.1299/jamdsm.4.1221

    Article  Google Scholar 

  46. Boettjer T, Krogshave JT, Ramanujan D (2021) Machine-specific estimation of milling energy consumption in detailed design. ASME J Manuf Sci Eng 43:081010–081011. https://doi.org/10.1115/1.4050179

    Article  Google Scholar 

  47. Lv J, Tang R, Tang W, Jia S, Liu Y, Cao Y (2018) An investigation into methods for predicting material removal energy consumption in turning. J Clean Prod 193:128–139. https://doi.org/10.1016/j.jclepro.2018.05.035

    Article  Google Scholar 

  48. Sihag N (2020) An experimental analysis of energy consumption and environmental impacts of milling process, PhD thesis, Birla Institute of Technology and Science Pilani

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Funding

The authors thank and acknowledge the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India for providing financial support to carry out this research work (Project No: SB/FTP/ETA-03/2013).

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SSP: original draft writing, investigation, performing experimentation, validation, resources, software. TCB: conceptualization, investigation, methodology, formal analysis, reviewing, supervision. KSS: investigation, reviewing, supervision.

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Correspondence to Tufan Chandra Bera.

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Highlights

• Energy consumption model for milling of variable curved geometries is hybrid in nature.

• Cutting power consumption is expressed as a function of cutting and feed forces.

• Cutting power consumption is also a function of workpiece curvature in milling.

• Proposed model is more generic and applicable for straight, circular and variable curved geometries.

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Pawar, S.S., Bera, T.C. & Sangwan, K.S. Energy consumption modelling in milling of variable curved geometry. Int J Adv Manuf Technol 120, 1967–1987 (2022). https://doi.org/10.1007/s00170-022-08854-5

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  • DOI: https://doi.org/10.1007/s00170-022-08854-5

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