Skip to main content
Log in

An improved thermal performance modeling for high-speed spindle of machine tool based on thermal contact resistance analysis

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

Abstract

The temperature field distribution has a significant influence on structural performance, thermal deformation, and thermal error compensation. To improve the prediction accuracy of the temperature distribution of the spindle system, a comprehensive model considering the contact thermal resistance (TCR) of the interfaces was established to analyze the thermal performance of the high-speed spindle system in the present work. An elastoplastic contact model was used to calculate the contacting areas and loads of interfaces, which were employed to establish the contact thermal resistance model of the primary interfaces of the spindle, such as bearing rings and tool holders. Based on the TCR parameters, a finite element analysis (FEA) model was proposed to analyze the temperature distribution of the spindle system. And a temperature test experiment was set up to verify the accuracy of the FEA model. The results show that the relative errors of representative test points were all less than 5%, which means the established model can appropriately reflect the temperature field distribution of the spindle.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Aalilija A, Gandin CA, Hachem E (2021) A simple and efficient numerical model for thermal contact resistance based on diffuse interface immersed boundary method. Int J Therm Sci 166:1–11. https://doi.org/10.1016/j.ijthermalsci.2020.106817

    Article  Google Scholar 

  2. Abele E, Altintas Y, Brecher C (2010) Machine tool spindle units. CIRP Ann 59(2):781–802. https://doi.org/10.1016/j.cirp.2010.05.002

    Article  Google Scholar 

  3. Cao H, Zhang X, Chen X (2017) The concept and progress of intelligent spindles: a review. Int J Mach Tools Manuf 112:21–52. https://doi.org/10.1016/j.ijmachtools.2016.10.005

    Article  Google Scholar 

  4. 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. https://doi.org/10.1016/j.applthermaleng.2015.04.064

    Article  Google Scholar 

  5. Liu J, Ma C, Wang S, Wang S, Yang B, Shi H (2019) Thermal-structure interaction characteristics of a high-speed spindle- bearing system. Int J Mach Tools Manuf 137:42–57. https://doi.org/10.1016/j.ijmachtools.2018.10.004

    Article  Google Scholar 

  6. Zhang L, Li C, Wu Y, Zhang K, Shi H (2017) Hybrid prediction model of the temperature field of a motorized spindle. Appl Sci 7(10):1091–1104. https://doi.org/10.3390/app7101091

    Article  Google Scholar 

  7. Tan F, Yin Q, Dong G, Xie L, Yin G (2017) An optimal convective heat transfer coefficient calculation method in thermal analysis of spindle system. Int J Adv Manuf Technol 91(5–8):2549–2560. https://doi.org/10.1007/s00170-016-9924-2

    Article  Google Scholar 

  8. Blaser P, Pavliček F, Mori K, Mayr J, Weikert S, Wegener K (2017) Adaptive learning control for thermal error compensation of 5-axis machine tools. J Manuf Syst 44:302–309. https://doi.org/10.1016/j.jmsy.2017.04.011

    Article  Google Scholar 

  9. Li Y, Zhao W, Lan S, Ni J, Wu W, Lu B (2015) A review on spindle thermal error compensation in machine tools. Int J Mach Tools Manuf 95:20–38. https://doi.org/10.1016/j.ijmachtools.2015.04.008

    Article  Google Scholar 

  10. Grama SN, Mathur A, Badhe AN (2018) A model-based cooling strategy for motorized spindle to reduce thermal errors. Int J Mach Tools Manuf 132:3–16. https://doi.org/10.1016/j.ijmachtools.2018.04.004

    Article  Google Scholar 

  11. Liu T, Gao W, Zhang D, Zhang Y, Chang W, Liang C, Tian Y (2017) Analytical modeling for thermal errors of motorized spindle unit. Int J Mach Tools Manuf 112:53–70. https://doi.org/10.1016/j.ijmachtools.2016.09.008

    Article  Google Scholar 

  12. Zheng D, Chen W, Li M (2018) An improved model on forecasting temperature rise of high-speed angular contact ball bearings considering structural constraints. Ind Lubric Tribol 70(1):15–22. https://doi.org/10.1108/ilt-06-2016-0133

    Article  Google Scholar 

  13. Liu J, Lai T, Tie G (2018) Influence of thermo-mechanical coupled behaviors on milling stability of high speed motorized spindles. Precis Eng 52:94–105. https://doi.org/10.1016/j.precisioneng.2017.11.011

    Article  Google Scholar 

  14. Zheng D, Chen W (2017) Thermal performances on angular contact ball bearing of high-speed spindle considering structural constraints under oil-air lubrication. Tribol Int 109:593–601. https://doi.org/10.1016/j.triboint.2017.01.035

    Article  Google Scholar 

  15. Liu J, Zhang P (2018) Thermo-mechanical behavior analysis of motorized spindle based on a coupled model. Adv Mech Eng 10(1):1–12. https://doi.org/10.1177/1687814017747144

    Article  MathSciNet  Google Scholar 

  16. Wu L, Tan Q (2016) Thermal characteristic analysis and experimental study of a spindle-bearing system. Entropy 18(7):271–295. https://doi.org/10.3390/e18070271

    Article  MathSciNet  Google Scholar 

  17. Lee J, Kim DH, Lee CM (2015) A study on the thermal characteristics and experiments of High-Speed spindle for machine tools. Int J Precis Eng Manuf 16(2):293–299. https://doi.org/10.1007/s12541-015-0039-8

    Article  Google Scholar 

  18. Sun L, Ren M, Hong H, Yin Y (2016) Thermal error reduction based on thermodynamics structure optimization method for an ultra-precision machine tool. Int J Adv Manuf Technol 88(5–8):1267–1277. https://doi.org/10.1007/s00170-016-8868-x

    Article  Google Scholar 

  19. Liu J, Ma C, Wang S, Wang S, Yang B (2019) Thermal contact resistance between bearing inner ring and shaft journal. Int J Therm Sci 138:521–535. https://doi.org/10.1016/j.ijthermalsci.2019.01.022

    Article  Google Scholar 

  20. Cui Y, Li H, Li T, Chen L (2018) An accurate thermal performance modeling and simulation method for motorized spindle of machine tool based on thermal contact resistance analysis. Int J Adv Manuf Technol 96(5–8):2525–2537. https://doi.org/10.1007/s00170-018-1593-x

    Article  Google Scholar 

  21. Yüncü H (2006) Thermal contact conductance of nominaly flat surfaces. Heat Mass Transf 43(1):1–5. https://doi.org/10.1007/s00231-006-0087-9

    Article  Google Scholar 

  22. Ji J, Hong R, Sun F, Huang X (2018) Thermal characteristic analysis of Z-axis guideway based on thermal contact resistance. Adv Mech Eng 10(10):1–14. https://doi.org/10.1177/1687814018805321

    Article  Google Scholar 

  23. Tanaka CB, Ballester RY, De Souza GM, Zhang Y, Meira JBC (2018) Influence of residual thermal stresses on the edge chipping resistance of PFM and veneered zirconia structures: Experimental and FEA study. Dent Mater 35(2):344–355. https://doi.org/10.1016/j.dental.2018.11.034

    Article  Google Scholar 

  24. Song H, Vakis AI, Liu X, Van der Giessen E (2017) Statistical model of rough surface contact accounting for size-dependent plasticity and asperity interaction. J Mech Phys Solids 106:1–14. https://doi.org/10.1016/j.jmps.2017.05.014

    Article  MathSciNet  Google Scholar 

  25. Xiao H, Sun Y (2019) On the normal contact stiffness and contact resonance frequency of rough surface contact based on asperity micro-contact statistical models. Eur J Mech A Solids 75:450–460. https://doi.org/10.1016/j.euromechsol.2019.03.004

    Article  MathSciNet  MATH  Google Scholar 

  26. Liu Y, Wang Y, Chen X, Yu H (2018) A spherical conformal contact model considering frictional and microscopic factors based on fractal theory. Chaos Solitons Fractals 111:96–107. https://doi.org/10.1016/j.chaos.2018.04.017

    Article  MathSciNet  Google Scholar 

  27. Gao Z, Fu W, Wang W, Kang W, Liu Y (2018) The study of anisotropic rough surfaces contact considering lateral contact and interaction between asperities. Tribol Int 126:270–282. https://doi.org/10.1016/j.triboint.2018.01.056

    Article  Google Scholar 

  28. Wen Y, Tang J, Zhou W, Zhu C (2018) An improved simplified model of rough surface profile. Tribol Int 125:75–84. https://doi.org/10.1016/j.triboint.2018.04.025

    Article  Google Scholar 

  29. Qin W, Jin X, Kirk A, Shipway PH, Sun W (2018) Effects of surface roughness on local friction and temperature distributions in a steel-on-steel fretting contact. Tribol Int 120:350–357. https://doi.org/10.1016/j.triboint.2018.01.016

    Article  Google Scholar 

  30. Greenwood JA, Williamson JBP (1966) Contact of nominally flat surfaces. Proc R Soc Lond 295(1442):300–319. https://doi.org/10.1098/rspa.1966.0242

    Article  Google Scholar 

  31. Kogut L, Etsion I (2002) Elastic-plastic contact analysis of a sphere and a rigid flat. J Appl Mech 69(5):657–662. https://doi.org/10.1115/1.1490373

    Article  MATH  Google Scholar 

  32. Lin LP, Lin JF (2005) An elastoplastic microasperity contact model for metallic materials. J Tribol 127(3):666–672. https://doi.org/10.1115/1.1843830

    Article  Google Scholar 

  33. Zhao Y, Maietta DM, Chang L (2000) An asperity microcontact model incorporating the transition from elastic deformation to fully plastic flow. J Tribol 122(1):86–93. https://doi.org/10.1115/1.555332

    Article  Google Scholar 

  34. Zheng D, Chen W, Zheng D (2021) An enhanced estimation on heat generation of angular contact ball bearings with vibration effect. Int J Therm Sci 159:106610. https://doi.org/10.1016/j.ijthermalsci.2020.106610

    Article  Google Scholar 

  35. Kiselev NA, Leontiev AI, Vinogradov YA, Zditovets AG, Strongin MM (2019) Effect of large-scale vortex induced by a cylinder on the drag and heat transfer coefficients of smooth and dimpled surfaces. Int J Therm Sci 136:396–409. https://doi.org/10.1016/j.ijthermalsci.2018.11.005

    Article  Google Scholar 

  36. Xian Y, Zhang P, Zhai S, Yuan P, Yang D (2018) Experimental characterization methods for thermal contact resistance: a review. Appl Therm Eng 130:1530–1548. https://doi.org/10.1016/j.applthermaleng.2017.10.163

    Article  Google Scholar 

Download references

Funding

This research was supported by National Natural Science Foundation of China (Grant No. 51605091 and 51605094), the National Natural Science Foundation of Fujian Province (Grant No. 2017J05073), and Regional Development Project of Fujian Province (Grant No. 2020H4028). The authors received financial support provided by the Science and Technology Innovation Special Fund of Fujian Agriculture and Forestry University and Longyan Yifeng Mechanical Science and Technology Co., Ltd.

Author information

Authors and Affiliations

Authors

Contributions

Bing Fang conceptualized the idea, designed the methodology, and undertook data curation, investigation, formal analysis, project administration, manuscript writing, reviewing, and editing; Tianqi Gu undertook writing, original draft, review, and editing; Mengna Cheng and Dapeng Ye undertook experiment, data curation and investigation, and acquired resources.

Corresponding author

Correspondence to Tianqi Gu.

Ethics declarations

Consent to participate and publish

The authors declare that they participated in this paper willingly, and the authors declare to consent to the publication of this paper.

Competing interests

The authors declare no competing interests.

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

Fang, B., Cheng, M., Gu, T. et al. An improved thermal performance modeling for high-speed spindle of machine tool based on thermal contact resistance analysis. Int J Adv Manuf Technol 120, 5259–5268 (2022). https://doi.org/10.1007/s00170-022-09085-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-022-09085-4

Keywords

Navigation