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Analysis of CNC machining based on characteristics of thermal errors and optimal design of experimental programs during actual cutting process

  • En-ming MiaoEmail author
  • Hui Liu
  • Kuang-chao Fan
  • Xuan-xuan Lv
  • Yi Liu
  • Yi Hu
ORIGINAL ARTICLE

Abstract

The compensation of thermal errors plays a critical role in developing the machine tools of intelligent computer numerically controlled (CNC). According to the international standards, the testing, modeling, and compensation of thermal error of CNC machine tools are carried out only in a so-called idling state where the spindle is free running without any workload. However, in practical applications, machine tools are often applied in the actual cutting state with more influence factors, such as cutting parameters, cooling liquid, and cutting force. Subsequently, the thermal characteristics at idling state and actual cutting state are compared and analyzed in this paper. It was found that the thermal error compensation model under idling state is not precise enough to be applied in actual cutting state. Also, further research finds that different combinations of cutting parameters, such as spindle speed and feed rate, also have influences on the accuracy of prediction and robustness of thermal error model under actual cutting state. Therefore, the cutting parameters of spindle speed, feed rate, depth of cut, and ambient temperature are studied with the usage of the Taguchi method. Through calculating signal-to-noise ratio (SN) of each combination through residual standard deviation of thermal error model, the combination of optimal cutting parameters can be obtained. The resultant analysis shows that the thermal error model under the combination of optimal cutting parameters demonstrates higher accuracy of prediction and better robustness.

Keywords

Thermal error compensation Actual cutting Taguchi method The optimal cutting parameters combination CNC 

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References

  1. 1.
    Bryan J (1990) International status of thermal error research. Ann CIRP 39(2):645–656CrossRefGoogle Scholar
  2. 2.
    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 Tool Manuf 95:20–38CrossRefGoogle Scholar
  3. 3.
    Yang S, Yuan J, Ni J (1996) Accuracy enhancement of a horizontal machining center by real-time error compensation. J Manuf Syst 15(2):113–124CrossRefGoogle Scholar
  4. 4.
    IS0 230-3 (2001) Test code for machine tools, part 3: determination of thermal effects. ISOGoogle Scholar
  5. 5.
    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
  6. 6.
    Xiang ST, Lu HX, Yang JG (2015) Thermal error prediction method for spindles in machine tools based on a hybrid model. J Eng Manuf 229(1):130–140CrossRefGoogle Scholar
  7. 7.
    Miao EM, Gong YY, Niu PC, Ji CZ, Chen HD (2013) Robustness of thermal error compensation modeling models of CNC machine tools. Int J Adv Manuf Technol 69:2593–2603CrossRefGoogle Scholar
  8. 8.
    Creighton E, Honegger A, Tulsian A, Mukhopadhyay D (2010) Analysis of thermal errors in a high-speed micro-milling spindle. Int J Mach Tool Manuf 50:386–393CrossRefGoogle Scholar
  9. 9.
    Zhang T, Ye WH, Shan YC (2015) Application of sliced inverse regression with fuzzy clustering for thermal error modeling of cnc machine tool. International Journal of Advanced Manufacturing Technology 1–11Google Scholar
  10. 10.
    Zhang Y, Yang JG, Jiang H (2012) Machine tool thermal error modeling and prediction by grey neural network. Int J Adv Manuf Technol 59:1065–1072CrossRefGoogle Scholar
  11. 11.
    Miao EM, Gong YY, Dang LC, Miao JC (2014) Temperature-sensitive point selection of thermal error model of CNC machining center. Int J Adv Manuf Technol 74(5):681–691Google Scholar
  12. 12.
    Miao EM, Niu PC, Fei YT, Yan Y (2011) Selecting temperature-sensitive points and modeling thermal errors of machine tools. Journal of the Chinese Society of Mechanical Engineers 32(6):559–565Google Scholar
  13. 13.
    Seber GAF, Lee AJ (2012) Linear regression analysis. John Wiley & Sons, New Jersy, USAGoogle Scholar
  14. 14.
    Chatterjee S, Hadi AS (2015) Regression analysis by example. John Wiley & Sons, New Jersy, USAGoogle Scholar
  15. 15.
    Taguchi G (1993) Taguchi on robust technology development: bring quality engineering upstream. ASME Press, New YorkCrossRefGoogle Scholar
  16. 16.
    Tarng YS, Yang WH (1998) Optimisation of the weld bead geometry in gas tungsten arc welding by the Taguchi method. Int J Adv Manuf Technol 14(14):549–554CrossRefGoogle Scholar
  17. 17.
    Mahapatra SS, Patnaik A (2007) Optimization of wire electrical discharge machining (WEDM) process parameters using Taguchi method. Int J Adv Manuf Technol 34(9):911–925CrossRefGoogle Scholar
  18. 18.
    Gok K, Gok A, Kisioglu Y (2015) Optimization of processing parameters of a developed new driller system for orthopedic surgery applications using Taguchi method. Int J Adv Manuf Technol 76(5–8):1437–1448CrossRefGoogle Scholar
  19. 19.
    Dehnad K (1989) Quality control, robust design, and the Taguchi method. Springer, USzbMATHGoogle Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • En-ming Miao
    • 1
    Email author
  • Hui Liu
    • 1
  • Kuang-chao Fan
    • 1
    • 2
  • Xuan-xuan Lv
    • 1
  • Yi Liu
    • 1
  • Yi Hu
    • 1
  1. 1.Instrument Science and Opto-electronics EngineeringHefei University of TechnologyHefeiChina
  2. 2.Department of Mechanical EngineeringNational Taiwan UniversityTaipei CityTaiwan

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