Skip to main content

Advertisement

SpringerLink
Log in

Exergy efficiency optimization model of motorized spindle system for high-speed dry hobbing

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

Abstract

High-speed dry hobbing (HSDH) has been regarded as an environmentally benign gear machining technique. Current research on energy efficiency of machine tool focuses on the energy efficiency for workpiece material removal but rarely involves the energy consumption required to control thermal stability. Due to the fact that thermal effect dominates the machining accuracy and accuracy consistency, especially in the dry machining process, the energy consumption for thermal stability control should be taken as useful energy consumption. In view of this, by taking the motorized spindle system (MSS) as the study objective, which is a core subsystem of machine tool enabling HSDH and characterizes intensive and inefficient energy consumption, an exergy-based method is proposed to evaluate the comprehensive energy efficiency of MSS. Furthermore, an exergy efficiency optimization model is proposed to maximize total exergy efficiency and minimize average temperature of MSS. A solution method integrating Pareto Dominant-based Multi-objective Simulated Annealing (PDMOSA) and Technique for Order of Preference by Similarity to an Ideal Solution (TOPSIS) is proposed to search the best solution of the optimization model. A case study is introduced to validate the proposed model and a final optimal solution is obtained at the total exergy efficiency of 53.5% with balanced temperature of 35.4 °C. The cooling water in MSS is identified to be a dominant factor that affects total exergy destruction. The presented model can give a reference to select appropriate process parameters of MSS for green and precision machining.

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Abbreviations

HSDH:

High-speed dry hobbing

MSS:

Motorized spindle system

MRR:

Material removal rate

SEC:

Specific energy consumption

sce:

Specific cutting energy

TEE:

Total exergy efficiency

A :

Area

brgco:

Bearing cover

C :

Specific heat capacity

ch:

Chip thickness

D :

Diameter

E :

Energy

Ex:

Exergy

\( \dot{E}x \) :

Exergy rate

F :

Force

f :

Feed rate

H :

Number of bearings

h :

Specific enthalpy

J :

Equivalent moment of inertia

k :

Specific heat ratio of compressed air

L :

Length

\( \dot{m} \) :

Mass flow rate

n :

Spindle speed

P :

Power

p :

Pressure

R :

Gas constant

r :

Compression ratio

\( \dot{S} \) :

Entropy rate

s :

Specific entropy

sphs:

Spindle housing

T :

Temperature

t :

Time

th:

Thickness

α :

Angular acceleration

ε :

Exergy efficiency

λ:

Heat conductivity

η :

Efficiency

ρ :

Density

acce:

Acceleration stage

acom:

Air after compressed

airc:

Air-cutting stage

airr:

Air refrigerator

am:

Spindle ambient

brg:

Bearing

ca:

Compressed air

ch:

Cooling groove

co:

Cooling device

cr:

Circulation pump

cut:

Cutting stage

cycle:

Machining cycle of a workpiece

dece:

Deceleration stage

el:

Electrical

idle:

Idle stage

in:

Input

mac:

Machine tool

motor:

Spindle motor

mr:

Material removal

out:

Output

rot:

Rotation

ss:

Spindle outer surface

sp:

Motorized spindle

tc:

Thermal conduction

th:

Thermal

tn:

Natural thermal convection

W:

Cooling water

wcp:

Water circulating pump

References

  1. Duflou JR, Sutherland JW, Dornfeld D, Herrmann C, Jeswiet J, Kara S, Hauschild M, Kellens K (2012) Towards energy and resource efficient manufacturing: a processes and systems approach. CIRP Ann Manuf Technol 61(2):587–609

    Google Scholar 

  2. Cai W, Liu F, Xie J, Liu PJ, Tuo JB (2017) A tool for assessing the energy demand and efficiency of machining systems: Energy benchmarking. Energy 138:332–347

    Google Scholar 

  3. ISO, ISO 14955-1: 2017 Machine tools -- Environmental evaluation of machine tools -- part 1: design methodology for energy-efficient machine tools. International Organization for Standardization. https://www.iso.org/standard/70035.html.

  4. Zhao GY, Liu ZY, He Y, Cao HJ, Guo YB (2017) Energy consumption in machining: classification, prediction, and reduction strategy. Energy 133:142–157

    Google Scholar 

  5. Cui YF, Geng ZQ, Zhu QX, Han YM (2017) Review: multi-objective optimization methods and application in energy saving. Energy 125:681–704

    Google Scholar 

  6. Zhou LR, Li JF, Li FY, Meng Q, Li J, Xu XS (2016) Energy consumption model and energy efficiency of machine tools: a comprehensive literature review. J Clean Prod 112:3721–3734

    Google Scholar 

  7. Kara S, Li W (2011) Unit process energy consumption models for material removal processes. CIRP Ann Manuf Technol 60(1):37–40

    Google Scholar 

  8. Balogun VA, Edem IF, Adekunle AA, Mativenga PT (2016) Specific energy based evaluation of machining efficiency. J Clean Prod 116:187–197

    Google Scholar 

  9. Tuo JB, Liu F, Liu PJ, Zhang H, Cai W (2018) Energy efficiency evaluation for machining systems through virtual part. Energy 159:172–183

    Google Scholar 

  10. Ghosh S, Chattopadhyay AB, Paul S (2008) Modelling of specific energy requirement during high-efficiency deep grinding. Int J Mach Tools Manuf 48:1242–1253

    Google Scholar 

  11. Wang LH, Wang W, Liu DW (2017) Dynamic feature based adaptive process planning for energy-efficient NC machining. CIRP Ann Manuf Technol 66:441–444

    Google Scholar 

  12. Xiao QG, Li CB, Tang Y, Li LL, Li L (2019) A knowledge-driven method of adaptively optimizing process parameters for energy efficient turning. Energy 166:142–156

    Google Scholar 

  13. Cao HJ, Zhu LB, Li XG, Chen P, Chen YP (2016) Thermal error compensation of dry hobbing machine tool considering workpiece thermal deformation. Int J Adv Manuf Technol 86(5-8):1739–1751

    Google Scholar 

  14. Mayr J, Jedrzejewski J, Uhlmann E, Donmez MA, Knapp W, Hartig F, Wendt K, Moriwaki T, Shore P, Schmitt R, Brecher C, Wurz T, Wegener K (2012) Thermal issues in machine tools. CIRP Ann Manuf Technol 61(2):771–791

    Google Scholar 

  15. Gupta K, Laubscher RF, Davim JP, Jain NK (2016) Recent developments in sustainable manufacturing of gears: a review. J Clean Prod 112:3320–3330

    Google Scholar 

  16. Yang X, Cao HJ, Zhu LB, Li BJ (2017) A 3D chip geometry driven predictive method for heat-loading performance of hob tooth in high-speed dry hobbing. Int J Adv Manuf Technol 93(5-8):1583–1594

    Google Scholar 

  17. Zhu LB, Cao HJ, Zeng D, Yang X, Li BJ (2017) Multi-variable driving thermal energy control model of dry hobbing machine tool. Int J Adv Manuf Technol 92(1-4):259–275

    Google Scholar 

  18. Albertelli P (2017) Energy saving opportunities in direct drive machine tool spindles. J Clean Prod 165:855–873

    Google Scholar 

  19. Abele E, Altintas Y, Brecher C (2010) Machine tool spindle units. CIRP Ann Manuf Technol 59(2):781–802

    Google Scholar 

  20. Wegener K, Mayr J, Merklein M, Behrens BA, Aoyama T, Sulitka M, Fleischer J, Groche P, Kaftanoglu B, Jochum N, Mohring HC (2017) Fluid elements in machine tools. CIRP Ann Manuf Technol 66(2):611–634

    Google Scholar 

  21. Li Y, Zhao WH, Lan SH, Ni J, Wu WW, Lu BH (2015) A review on spindle thermal error compensation in machine tools. Int J Mach Tools Manuf 95:20–38

    Google Scholar 

  22. Creighton E, Honegger A, Tulsian A, Mukhopadhyay D (2010) Analysis of thermal errors in a high-speed micro-milling spindle. Int J Mach Tools Manuf 50:386–393

    Google Scholar 

  23. Cengel YA, Boles MA (2010) Thermodynamics: an engineering approach (7th). McGraw-Hill Education, New York

    Google Scholar 

  24. Hosseinzadeh M, Sardarabadi M, Passandideh-Fard M (2018) Energy and exergy analysis of nanofluid based photovoltaic thermal system integrated with phase change material. Energy 147:636–647

    Google Scholar 

  25. Koroglu T, Sogut OS (2018) Conventional and advanced exergy analyses of a marine steam power plant. Energy 163:392–403

    Google Scholar 

  26. Zhang Q, Yi H, Yu Z, Gao J, Wang X, Lin H, Shen B (2018) Energy-exergy analysis and energy efficiency improvement of coal-fired industrial boilers based on thermal test data. Appl Therm Eng 144:614–627

    Google Scholar 

  27. Bühler F, Nguyen TV, Jensen JK, Holm FM, Elmegaard B (2018) Energy, exergy and advanced exergy analysis of a milk processing factory. Energy 162:576–592

    Google Scholar 

  28. Sharifzadeh M, Ghazikhani M, Niazmand H (2018) Temporal exergy analysis of adsorption cooling system by developing non-flow exergy function. Appl Therm Eng 139:409–418

    Google Scholar 

  29. Fellaou S, Bounahmidi T (2018) Analyzing thermodynamic improvement potential of a selected cement manufacturing process: advanced exergy analysis. Energy 154:190–200

    Google Scholar 

  30. Okwudire C, Rodgers J (2013) Design and control of a novel hybrid feed drive for high performance and energy efficient machining. CIRP Ann Manuf Technol 62(1):391–394

    Google Scholar 

  31. Chien CH, Jang JY (2008) 3-D numerical and experimental analysis of a built-in motorized high-speed spindle with helical water cooling channel. Appl Therm Eng 28(17-18):2327–2336

    Google Scholar 

  32. Bossmanns B, Tu JF (2001) A power flow model for high speed motorized spindles—heat generation characterization. J Manuf Sci Eng 123:494–505

    Google Scholar 

  33. Bossmanns B, Tu JF (1999) A thermal model for high speed motorized spindles. Int J Mach Tools Manuf 39(9):1345–1366

    Google Scholar 

  34. Ma C, Yang J, Zhao L, Mei X, Shi H (2015) Simulation and experimental study on the thermally induced deformations of high-speed spindle system. Appl Therm Eng 86:251–268

    Google Scholar 

  35. Kaushik SC, Manikandan S, Hans R (2015) Energy and exergy analysis of thermoelectric heat pump system. Int J Heat Mass Transf 86:843–852

    Google Scholar 

  36. Avram OI, Xirouchakis P (2011) Evaluating the use phase energy requirements of a machine tool system. J Clean Prod 19:699–711

    Google Scholar 

  37. Lv J, Tang R, Tang W, Liu Y, Zhang Y, Jia S (2017) An investigation into reducing the spindle acceleration energy consumption of machine tools. J Clean Prod 143:794–803

    Google Scholar 

  38. Zhu LB, Cao HJ, Huang HH, Yang X (2017) Exergy analysis and multi-objective optimization of air cooling system for dry machining. Int J Adv Manuf Technol 93(9-12):3175–3188

    Google Scholar 

  39. Pusavec F, Krajnik P, Kopac J (2010) Transition to sustainable production—part I: application on machining technologies. J Clean Prod 18:174–184

    Google Scholar 

  40. Weber J, Weber J, Shabi L, Lohse H (2016) Energy, power and heat flow of the cooling and fluid systems in a cutting machine tool. Procedia Cirp 46:99–102

    Google Scholar 

  41. Yang X, Cao H, Li B, Jafar S, Zhu L (2018) A thermal energy balance optimization model of cutting space enabling environmentally benign dry hobbing. J Clean Prod 172:2323–2335

    Google Scholar 

  42. Chen XZ, Li CB, 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(1-4):775–788

    Google Scholar 

  43. Balogun VA, Edem IF, Gu H, Mativenga PT (2018) Energy centric selection of machining conditions for minimum cost. Energy 164:655–663

    Google Scholar 

  44. Li BJ, Cao HJ, Yang X, Jafar S, Zeng D (2018) Thermal energy balance control model of motorized spindle system enabling high-speed dry hobbing process. J Manuf Process 35:29–39

    Google Scholar 

  45. Gunay M, Aslan E, Korkut I, Seker U (2004) Investigation of the effect of rake angle on main cutting force. Int J Mach Tools Manuf 44(9):953–959

    Google Scholar 

  46. Suman B (2004) Study of simulated annealing based algorithms for multiobjective optimization of a constrained problem. Comput Chem Eng 28(9):1849–1871

    Google Scholar 

  47. Sahin R, Turkbey O (2009) A simulated annealing algorithm to find approximate Pareto optimal solutions for the multi-objective facility layout problem. Int J Adv Manuf Technol 41(9-10):1003–1018

    Google Scholar 

  48. Li LL, Li CB, Ying T, Li L (2017) An integrated approach of process planning and cutting parameter optimization for energy-aware CNC Machining. J Clean Prod 162:458–473

    Google Scholar 

  49. Chyu CC, Chang WS (2011) Optimizing fuzzy makespan and tardiness for unrelated parallel machine scheduling with archived metaheuristics. Int J Adv Manuf Technol 57(5-8):763–776

    Google Scholar 

  50. Hoseini P, Shayesteh MG (2013) Efficient contrast enhancement of images using hybrid ant colony optimisation, genetic algorithm, and simulated annealing. Digit Signal Process 23(3):879–893

    MathSciNet  Google Scholar 

  51. Yue ZL (2011) A method for group decision-making based on determining weights of decision makers using TOPSIS. Appl Math Model 35(4):1926–1936

    MathSciNet  MATH  Google Scholar 

  52. Shirazi A, Najafi B, Aminyavari M, Rinaldi F, Taylor RA (2014) Thermal-economic-environmental analysis and multi-objective optimization of an ice thermal energy storage system for gas turbine cycle inlet air cooling. Energy 69:212–226

    Google Scholar 

  53. Sayyaadi H, Mehrabipour R (2012) Efficiency enhancement of a gas turbine cycle using an optimized tubular recuperative heat exchanger. Energy 38:362–375

    Google Scholar 

  54. Lin YK, Yeh CT (2012) Multi-objective optimization for stochastic computer networks using NSGA-II and TOPSIS. Eur J Oper Res 218(3):735–746

    MathSciNet  MATH  Google Scholar 

  55. Tavana M, Li Z, Mobin M, Komaki M, Teymourian E (2016) Multi-objective control chart design optimization using NSGA-III and MOPSO enhanced with DEA and TOPSIS. Expert Syst Appl 50:17–39

    Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China (grant number 51475058); the Chang Jiang Scholars Program of China (grant number Q2015150); the Scientific and Technological Innovation Leading Talents Program of National “Ten-thousand People Plan” of China; and the Fundamental Research Funds for the Central University of China (grant number 2018CDJDC0001).

Author information

Authors and Affiliations

  1. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044, China

    Benjie Li, Huajun Cao, Hu Liu, Dan Zeng & Erheng Chen

  2. Army Engineering University of PLA, Xuzhou, 221000, Jiangsu, China

    Hu Liu

Authors
  1. Benjie Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huajun Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dan Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Erheng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huajun Cao.

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

Li, B., Cao, H., Liu, H. et al. Exergy efficiency optimization model of motorized spindle system for high-speed dry hobbing. Int J Adv Manuf Technol 104, 2657–2668 (2019). https://doi.org/10.1007/s00170-019-04134-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-019-04134-x

Keywords

Search

Navigation