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The modeling method on thermal expansion of CNC lathe headstock in vertical direction based on MOGA

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Abstract

Thermal errors often occur in spindle system due to the changing of ambient temperature and/or inner or outer heat sources of machine tools (MTs), which is the major factor restricting the machine accuracy. Therefore, constructing compensation models with high accuracy and high robustness turn to be a cost-efficient approach for minimizing thermal error and improving machine accuracy. There are two categories of modeling methods for thermal errors so far, namely, physical-based method and empirical-based method. Each modeling method has its own merits and drawbacks. In this paper, a multi-objective genetic algorithm (MOGA) was used to combine the approximative physical model based on thermal expansion mechanism in vertical direction of numerical control (NC) lathe headstock with the accurate empirical model based on experimental data obtained from thermal performance test of spindle system. Consequently, a prediction model of thermal error in vertical direction of headstock with high accuracy and high robustness was obtained. The new model synthesized the advantages of the two types of modeling methods and showed relatively high accuracy and robustness. According to the results of a series of verification experiments on thermal performance of the spindle system under various ambient temperatures and different working conditions, the precision of prediction model has maintained above 74%.

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References

  1. Bryan J (1990) International status of thermal error research. CIRP Ann 39(2):645–656. https://doi.org/10.1016/S0007-8506(07)63001-7

    Article  Google Scholar 

  2. Mayr J, Jedrzejewski J, Uhlmann E, Alkan Donmez M, Knapp W, Härtig F, Wendt K, Moriwaki T, Shore P, Schmitt R, Brecher C, Würz T, Wegener K (2012) Thermal issues in machine tools. CIRP Ann 61(2):771–791. https://doi.org/10.1016/j.cirp.2012.05.008

    Article  Google Scholar 

  3. Ni J (1997) CNC machine accuracy enhancement through real-time error compensation. J Manuf Sci Eng 119:717–725. https://doi.org/10.1115/1.2836815

    Article  Google Scholar 

  4. Liu K, Li T, Wang Y, Sun M, Wu Y, Zhu T (2018) Physically based modeling method for comprehensive thermally induced errors of CNC machining centers. Int J Adv Manuf Technol 94(1–4):463–474. https://doi.org/10.1007/s00170-017-0736-9

    Article  Google Scholar 

  5. Krulewich DA (1998) Temperature integration model and measurement point selection for thermally induced machine tool errors. MECHATRONICS 8(4):395–412. https://doi.org/10.1016/S0957-4158(97)00059-7

    Article  Google Scholar 

  6. Zhao H, Yang J, Shen J (2007) Simulation of thermal behavior of a CNC machine tool spindle. Int J Mach Tools Manuf 47(6):1003–1010. https://doi.org/10.1016/j.ijmachtools.2006.06.018

    Article  Google Scholar 

  7. Li Y, Zhao W, Wu W, Lu B (2017) Boundary conditions optimization of spindle thermal error analysis and thermal key points selection based on inverse heat conduction. Int J Adv Manuf Technol 90(9–12):2803–2812. https://doi.org/10.1007/s00170-016-9594-0

    Article  Google Scholar 

  8. Tseng P (1997) A real-time thermal inaccuracy compensation method on a machining centre. Int J Adv Manuf Technol 13:182–190. https://doi.org/10.1007/bf01305870

    Article  Google Scholar 

  9. Miao EM, Wang X, Fei YT, Yan Y (2011) Application of autoregressive distributed lag (ADL) model to thermal error modeling on NC machine tools. Appl Mech Mater 103:9–14. https://doi.org/10.4028/www.scientific.net/AMM.103.9

    Article  Google Scholar 

  10. Chen JS, Chiou G (1995) Quick testing and modeling of thermally-induced errors of CNC machine tools. Int J Mach Tools Manuf 35(7):1063–1074. https://doi.org/10.1016/0890-6955(94)00101-O

    Article  Google Scholar 

  11. Chen J (1996) A study of thermally induced machine tool errors in real cutting conditions. Int J Mach Tools Manuf 36(12):1401–1411. https://doi.org/10.1016/0890-6955(95)00096-8

    Article  Google Scholar 

  12. Yang S, Yuan J, Ni J, More S (1996) The improvement of thermal error modeling and compensation on machine tools by CMAC neural network. Int J Mach Tools Manuf 36(4):527–537. https://doi.org/10.1016/0890-6955(95)00040-2

    Article  Google Scholar 

  13. Mize CD, Ziegert JC (2000) Neural network thermal error compensation of a machining center. Precis Eng 24(4):338–346. https://doi.org/10.1016/S0141-6359(00)00044-1

    Article  Google Scholar 

  14. Liang R, Ye W, Zhang HH, Yang Q (2012) The thermal error optimization models for CNC machine tools. Int J Adv Manuf Technol 63(9–12):1167–1176. https://doi.org/10.1007/s00170-012-3978-6

    Google Scholar 

  15. Wu H, Zhang H, Guo Q, Wang X, Yang J (2008) Thermal error optimization modeling and real-time compensation on a CNC turning center. J Mater Process Tech 207(1–3):172–179. https://doi.org/10.1016/j.jmatprotec.2007.12.067

    Google Scholar 

  16. Zhang Y, Yang J, Jiang H (2012) Machine tool thermal error modeling and prediction by grey neural network. Int J Adv Manuf Technol 59(9–12):1065–1072. https://doi.org/10.1007/s00170-011-3564-3

    Article  Google Scholar 

  17. Ramesh R, Mannan MA, Poo AN (2002) Support vector machines model for classification of thermal error in machine tools. Int J Adv Manuf Technol 20(2):114–120. https://doi.org/10.1007/s001700200132

    Article  Google Scholar 

  18. Hong Y, Ni J (2003) Dynamic modeling for machine tool thermal error compensation. J Manuf Sci Eng 125(2):245–254. https://doi.org/10.1115/1.1557296

    Article  Google Scholar 

  19. Horejš O, Mareš M, Hornych J (2014) A general approach to thermal error modelling of machine tools. Machines et usinage a grande vitesse (MUGV), Clermont Ferrand, France

  20. Brecher C, Shneor Y, Neus S, Bakarinow K, Fey M (2015) Thermal behavior of externally driven spindle: experimental study and modelling. Engineering 07(02):73–92. https://doi.org/10.4236/eng.2015.72007

    Article  Google Scholar 

  21. Hou R, Yan Z, Du H, Chen T, Tao T, Mei X (2018) The application of multi-objective genetic algorithm in the modeling of thermal error of NC lathe. Procedia CIRP 67:332–337. https://doi.org/10.1016/j.procir.2017.12.222

    Article  Google Scholar 

  22. Sun M, Yang Z, Li W, Liu Q, Guo J (2010) An improved thermal simulation model for the spindle of CNC machine tool. International Conference on Mechanic Automation and Control Engineering, 2010. IEEE, 187–190. https://doi.org/10.1109/MACE.2010.5535576

  23. Yang S, Tao W (2006) Heat transfer theory (4ed.). Higher Education Press, Beijing

    Google Scholar 

  24. Incropera FP, DeWitt DP, Bergman TL (2014) Principles of heat and mass transfer. Wiley, New York

    Google Scholar 

  25. Vedder JD (1987) Simple approximations for the error function and its inverse. Am J Phys 55(8):762–763. https://doi.org/10.1119/1.15018

    Article  Google Scholar 

  26. International standard ISO 230-3 (2007) Test code for machine tools - part 3: determination of thermal effects. (2ed.). Switzerland. https://www.iso.org/standard/39188.html

  27. Wang L, Ng AHC, Kalyanmoy D (2011) Multi-objective evolutionary optimisation for product design and manufacturing. Springer, London. https://doi.org/10.1007/978-0-85729-652-8

    Book  Google Scholar 

  28. Srinivas N, Deb K (1994) Multiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221–248. https://doi.org/10.1162/evco.1994.2.3.221

    Article  Google Scholar 

  29. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197. https://doi.org/10.1109/4235.996017

    Article  Google Scholar 

  30. Zio E, Bazzo R (2012) A comparison of methods for selecting preferred solutions in multiobjective decision making. In: C Kahraman (Ed.), Computational intelligence Systems in Industrial Engineering (6) 23–43. Atlantis Press. Paris. https://doi.org/10.2991/978-94-91216-77-0_2

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Acknowledgements

The author would like to thank the anonymous referees and editors for their valuable comments and suggestions.

Funding

This project is supported by the National Natural Science Foundation of China (Grant No. 51775422).

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Correspondence to Tao Tao.

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Hou, R., Du, H., Yan, Z. et al. The modeling method on thermal expansion of CNC lathe headstock in vertical direction based on MOGA. Int J Adv Manuf Technol 103, 3629–3641 (2019). https://doi.org/10.1007/s00170-019-03728-9

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  • DOI: https://doi.org/10.1007/s00170-019-03728-9

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