A fast way to determine temperature sensor locations in thermal error compensation

  • Zhengchun Du
  • Xiaodong Yao
  • Hongfu Hou
  • Jianguo Yang
ORIGINAL ARTICLE
  • 31 Downloads

Abstract

With the improvement of machining accuracy, problems associated with the thermal deformation of the machine tool structure and the thermal error compensation technique have received more and more attention. However, the complexity of the predictive thermal model limits the application of the thermal error compensation technique. Appropriate temperature sensor locations could contribute to a better thermal model and greatly reduce the amount of time spent. This paper introduces a fast way to determine temperature sensor locations for thermal error compensation. A theoretical analysis of the heat transfer, heat exchange, and thermal deformation of a 1-D structure, i.e., a spindle, is discussed. A simulation of the heat transfers and exchange process for the spindle is performed, considering different heat flux coefficients and heat transfer coefficients. The results from the theoretical analysis and the simulation indicate that there is an optimal sensor location point on the 1-D structure and that the heat flux and transfer coefficients have little influence on the position of the optimal sensor location on the 1-D structure. The experimental results prove that optimal temperature sensor locations do exist, regarding which the temperature change and the spindle thermal deformation also have a nearly linear relationship without a time delay. Thus, a linear model can be obtained via interpolation of the experimental data. Finally, the optimal temperature sensor location method is successfully applied for the thermal error compensation of a high-speed spindle of a horizontal machining center.

Keywords

Thermal error Temperature sensor Optimal position Linear model Compensation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aronson RB (1996). The war against thermal expansion. Manuf Eng 116(6):45Google Scholar
  2. 2.
    Hsieh K-H, Chen T-R, Chang P, Tang C-H (2013) Thermal growth measurement and compensation for integrated spindles. Int J Adv Manuf Technol 64(5–8):889–901CrossRefGoogle Scholar
  3. 3.
    Cao H, Zhang X, Chen X (2016) The concept and progress of intelligent spindles: a review. Int J Mach Tools Manuf 112:21–52CrossRefGoogle Scholar
  4. 4.
    Sun L, Ren M, Hong H, Yin Y (2017) Thermal error reduction based on thermodynamics structure optimization method for an ultra-precision machine tool. Int J Adv Manuf Technol 88(5–8):1267–1277CrossRefGoogle Scholar
  5. 5.
    Abdulshahed AM, Longstaff AP, Fletcher S, Myers A (2015) Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera. Appl Math Model 39(7):1837–1852CrossRefGoogle Scholar
  6. 6.
    Ramesh R, Mannan MA, Poo AN (2000) Error compensation in machine tools — a review: Part II: thermal errors. Int J Mach Tools Manuf 40 (9):1257–1284CrossRefGoogle Scholar
  7. 7.
    Cengel Y (2014). Heat and mass transfer: fundamentals and applications. McGraw-Hill Higher EducationGoogle Scholar
  8. 8.
    Jiang S, Zhao Z, Sun M, Guo J, Yu H (2013) Analysis on thermal dynamic characteristics of CNC machine tool spindle. J Tianjin Univ 46(9):846–850Google Scholar
  9. 9.
    Xiang S, Zhu X, Yang J (2014) Modeling for spindle thermal error in machine tools based on mechanism analysis and thermal basic characteristics tests. Proc Inst Mech Eng C J Mech Eng Sci 228(18):3381–3394CrossRefGoogle Scholar
  10. 10.
    Bossmanns B, Tu JF (2001) A power flow model for high speed motorized spindles—heat generation characterization. J Manuf Sci Eng 123(3):494–505CrossRefGoogle Scholar
  11. 11.
    Mayr J, Ess M, Weikert S., Wegener, K (2009). Compensation of thermal effects on machine tools using a FDEM simulation approach. Proceedings Lamdamap, 9Google Scholar
  12. 12.
    Li Y, Zhao W (2012, August). Axial thermal error compensation method for the spindle of a precision horizontal machining center. In Mechatronics and Automation (ICMA), 2012 International Conference on (pp. 2319–2323). IEEE.Google Scholar
  13. 13.
    Han J, Wang L, Wang H, Cheng N (2012) A new thermal error modeling method for CNC machine tools. Int J Adv Manuf Technol 62(1–4):205–212CrossRefGoogle Scholar
  14. 14.
    Guo Q, Yang J (2011) Application of projection pursuit regression to thermal error modeling of a CNC machine tool. Int J Adv Manuf Technol 55(5–8):623–629Google Scholar
  15. 15.
    Huang Y, Zhang J, Li X, Tian L (2014) Thermal error modeling by integrating GA and BP algorithms for the high-speed spindle. Int J Adv Manuf Technol 71(9–12):1669–1675CrossRefGoogle Scholar
  16. 16.
    Yang H, Ni J (2005) Dynamic neural network modeling for nonlinear, nonstationary machine tool thermally induced error. Int J Mach Tools Manuf 45(4):455–465CrossRefGoogle Scholar
  17. 17.
    Yang Z, Sun M, Li W, Liang W (2011) Modified Elman network for thermal deformation compensation modeling in machine tools. Int J Adv Manuf Technol 54(5–8):669–676CrossRefGoogle Scholar
  18. 18.
    Abdulshahed AM, Longstaff AP, Fletcher S (2015) The application of ANFIS prediction models for thermal error compensation on CNC machine tools. Appl Soft Comput 27(7):158–168CrossRefGoogle Scholar
  19. 19.
    Zhang Y, Jiang H (2012) Machine tool thermal error modeling and prediction by grey neural network. Int J Adv Manuf Technol 59(9–12):1065–1072CrossRefGoogle Scholar
  20. 20.
    Du ZC, Yao SY, Yang JG (2015) Thermal behavior analysis and thermal error compensation for motorized spindle of machine tools. Int J Precision Eng Manuf 16(7):1571–1581CrossRefGoogle Scholar
  21. 21.
    Liu K, Liu Y, Sun M, Li X, Wu Y (2016) Spindle axial thermal growth modeling and compensation on CNC turning machines. Int J Adv Manuf Technol 87(5–8):1–8Google Scholar
  22. 22.
    Liu K, Liu Y, Sun MJ, Wu YL, Zhu TJ (2017) Comprehensive thermal growth compensation method of spindle and servo axis error on a vertical drilling center. Int J Adv Manuf Technol 88(9–12):2507–2516CrossRefGoogle Scholar
  23. 23.
    Yan JY, Yang JG (2009) Application of synthetic grey correlation theory on thermal point optimization for machine tool thermal error compensation. Int J Adv Manuf Technol 43(11–12):1124–1132CrossRefGoogle Scholar
  24. 24.
    Miao E, Liu Y, Liu H, Gao Z, Li W (2015) Study on the effects of changes in temperature-sensitive points on thermal error compensation model for CNC machine tool. Int J Mach Tools Manuf 97:50–59CrossRefGoogle Scholar
  25. 25.
    Cheng Q, Qi Z, Zhang G, Zhao Y, Sun B, Gu P (2016) Robust modelling and prediction of thermally induced positional error based on grey rough set theory and neural networks. Int J Adv Manuf Technol 83(5–8):753–764CrossRefGoogle Scholar
  26. 26.
    Han J, Wang L, Cheng N, Wang H (2012) Thermal error modeling of machine tool based on fuzzy c -means cluster analysis and minimal-resource allocating networks. Int J Adv Manuf Technol 60(5–8):463–472CrossRefGoogle Scholar
  27. 27.
    Tan F, Yin M, Wang L, Yin G (2018). Spindle thermal error robust modeling using LASSO and LS-SVM. Int J Adv Manuf Technol 94(5–8):2861–2874CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Zhengchun Du
    • 1
  • Xiaodong Yao
    • 1
  • Hongfu Hou
    • 1
  • Jianguo Yang
    • 1
  1. 1.School of Mechanical EngineeringShanghai Jiaotong UniversityShanghaiChina

Personalised recommendations