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Thermal deformation analysis and compensation of the direct-drive five-axis CNC milling head

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Abstract

The machining precision of the milling head is primarily affected by the thermal errors that originated from the thermal deformation. Thermal error compensation is an economical and efficient method to overcome these thermal errors. The milling head’s heat source is analyzed to calculate the thermal boundary load based on component parameters of the milling head. The milling head’s thermal deformation is then simulated using ANSYS software to achieve the milling head’s temperature distribution and the amount of thermal deformation. Through the design and construction of the milling head temperature and thermal deformation experiment platform, the thermal deformation experiment of the milling head is performed. Accordingly, the measuring point temperature and the tooltip offset are obtained. Finally, a thermal error compensation method is proposed based on the homogeneous transformation. The research results give a theoretical reference and technical support for the thermal error compensation, optimized design, and development of milling heads.

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References

  1. P. B. Zhao, J. Huang and Y. Y. Shi, Nonlinear dynamics of the milling head drive mechanism in five-axis CNC machine tools, International Journal of Advanced Manufacturing Technology, 91 (2017) 3195–3210.

    Article  Google Scholar 

  2. G. Fu, H. Gong, H. Gao, T. Gu and Z. Cao, Integrated thermal error modeling of machine tool spindle using a chicken swarm optimization algorithm-based radial basic function neural network, International Journal of Advanced Manufacturing Technology, 105 (5–6) (2019) 2039–2055.

    Article  Google Scholar 

  3. M. Gebhardt, J. Mayr, N. Furrer, T. Widmer, S. Weikert and W. Knapp, High precision grey-box model for compensation of thermal errors on five-axis machines, CIRP Annals-Manufacturing Technology, 63 (1) (2014) 509–512.

    Article  Google Scholar 

  4. J. Mayr, J. Jedrzejewski, E. Uhlmann, M. A. Donmez and K. Wegener, Thermal issues in machine tools, CIRP Annals-Manufacturing Technology, 61 (2) (2012) 771–791.

    Article  Google Scholar 

  5. Y. Li, W. Zhao, S. Lan, J. Ni and B. Lu, A review on spindle thermal error compensation in machine tools, International Journal of Advanced Manufacturing Technology, 95 (99) (2015) 20–38.

    Google Scholar 

  6. A. E. Ouafi, M. Guillot and N. Barka, An integrated modeling approach for ANN-based real-time thermal error compensation on a CNC turning center, Advanced Materials Research, 664 (2013) 907–915.

    Article  Google Scholar 

  7. R. Ramesh, M. A. Mannan and A. N. Poo, Thermal error measurement and modelling in machine tools, International Journal of Advanced Manufacturing Technology, 43 (4) (2003) 391–404.

    Google Scholar 

  8. A. Abdulshahed, M. Ali and S. Longstaff, The application of ANFIS prediction models for thermal error compensation on CNC machine tools, Applied Soft Computing, 27 (2015) 158–168.

    Article  Google Scholar 

  9. H. Pahk and S. W. Lee, Thermal error measurement and real time compensation system for the CNC machine tools incorporating the spindle thermal error and the feed axis thermal error, International Journal of Advanced Manufacturing Technology, 20 (7) (2002) 487–494.

    Article  Google Scholar 

  10. J. Mayr, M. Müller and S. Weikert, Automated thermal main spindle & B-axis error compensation of 5-axis machine tools, CIRP Annals-Manufacturing Technology, 65 (1) (2016) 479–482.

    Article  Google Scholar 

  11. P. Blaser, F. Pavliček, K. Mori, J. Mayr and K. Wegener, Adaptive learning control for thermal error compensation of 5-axis machine tools, Journal of Manufacturing Systems, 44 (2007) 302–309.

    Article  Google Scholar 

  12. M. Martin, H. Otakar and H. Luká, Thermal error compensation of a 5-axis machine tool using indigenous temperature sensors and CNC integrated python code validated with a machined test piece, Precision Engineering, 66 (2020) 21–30.

    Article  Google Scholar 

  13. T. Li, F. Li, J. Yao and H. Wang, Thermal error modeling and compensation of a heavy gantry-type machine tool and its verification in machining, International Journal of Advanced Manufacturing Technology, 92 (2017) 3073–3092.

    Article  Google Scholar 

  14. Y. Liu, E. Miao and H. Liu, Robust machine tool thermal error compensation modelling based on temperature-sensitive interval segmentation modelling technology, International Journal of Advanced Manufacturing Technology, 106 (2) (2020) 655–669.

    Article  Google Scholar 

  15. X. Wei, E. Miao, H. Liu, S. Liu and S. Chen, Two-dimensional thermal error compensation modeling for worktable of CNC machine tools, International Journal of Advanced Manufacturing Technology, 101 (2018) 501–509.

    Article  Google Scholar 

  16. Z. Li, J. Yang, K. Fan and Y. Zhang, Integrated geometric and thermal error modeling and compensation for vertical machining centers, International Journal of Advanced Manufacturing Technology, 76 (5–8) (2015) 1139–1150.

    Article  Google Scholar 

  17. X. D. Zhuang, H. Liu, E. M. Miao and X. Y. Wei, Robust modeling method for thermal error of CNC machine tools based on ridge regression algorithm, International Journal of Machine Tools and Manufacture, 113 (2017) 35–48.

    Article  Google Scholar 

  18. J. Mayr, P. Blaser, A. Ryser and P. Hernandez-Becerroa, An adaptive self-learning compensation approach for thermal errors on 5-axis machine tools handling an arbitrary set of sample rates, CIRP Annals-Manufacturing Technology, 67 (1) (2018) 551–554.

    Article  Google Scholar 

  19. B. Denkena, B. Bergmann and H. Klemme, Cooling of motor spindles-a review, International Journal of Advanced Manufacturing Technology, 110 (11–12) (2020) 1–22.

    Google Scholar 

  20. J. Lee, D. H. Kim and C. M. Lee, A study on the thermal characteristics and experiments of high-speed spindle for machine tools, International Journal of Precision Engineering and Manufacturing, 16 (2) (2015) 293–299.

    Article  Google Scholar 

  21. X. Zhang, S. Yu and P. Lou, Thermal error exponential model of CNC machine tools motorized spindle based on mechanism analysis, 2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, China (2019) 291–295.

  22. P. Zheng, X. Bao and F. Cui, Analysis on the thermal error compensation model of direct-drive A/C Bi-Rotary milling head, Applied Mechanics and Materials, 87 (2011) 59–62.

    Article  Google Scholar 

  23. P. Zheng and L. Liu, Thermal error modeling and compensation of the direct driving type A/C Axis CNC milling head, Applied Mechanics and Materials, 220–223 (2012) 333–338.

    Article  Google Scholar 

  24. F. Tan, C. Deng, H. Xiao, J. Luo and S. Zhao, A wrapper approach-based key temperature point selection and thermal error modeling method, International Journal of Advanced Manufacturing Technology, 106 (3–4) (2020) 1–14.

    Google Scholar 

  25. Y. Li, J. Zhao, S. Ji and F. Liang, The selection of temperature-sensitivity points based on K-harmonic means clustering and thermal positioning error modeling of machine tools, International Journal of Advanced Manufacturing Technology, 100 (2019) 2333–2348.

    Article  Google Scholar 

  26. Y. X. Li, J. G. Yang, T. Gelvis and Y. Y. Li, Optimization of measuring points for machine tool thermal error based on grey system theory, International Journal of Advanced Manufacturing Technology, 35 (7–8) (2008) 745–750.

    Article  Google Scholar 

  27. J. Y. Yan and J. G. Yang, Application of synthetic grey correlation theory on thermal point optimization for machine tool thermal error compensation, International Journal of Advanced Manufacturing Technology, 43 (11–12) (2009) 1124–1132.

    Article  Google Scholar 

  28. E. M. Miao, Y. Y. Gong, L. C. Dang and J. C. Miao, Temperature-sensitive point selection of thermal error model of CNC machining center, International Journal of Advanced Manufacturing Technology, 74 (2014) 681–691.

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by Research Program supported by the Harbin University of Science and Technology-INNA High Speed Motorized Spindle Joint Laboratory Research Project and National Natural Science Foundation of China, No. 51675145, China.

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Authors and Affiliations

  1. Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin, 150080, China

    Yaonan Cheng, Xianpeng Zhang, Guangxin Zhang, Wenqi Jiang & Baowei Li

Authors
  1. Yaonan Cheng
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  2. Xianpeng Zhang
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  3. Guangxin Zhang
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  4. Wenqi Jiang
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  5. Baowei Li
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Corresponding author

Correspondence to Yaonan Cheng.

Additional information

Yaonan Cheng is a Professor of the School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, China. His current research focuses on metal cutting theory and tool technology, high-speed cutting technology and efficient machining technology for difficult-to-machine materials.

Xianpeng Zhang is a master’s student of Harbin University of Science and Technology, Harbin, China. His current research focuses on high-speed cutting technology, metal cutting principles and tools.

Guangxin Zhang is a master’s student of Harbin University of Science and Technology. His main research focuses on high-speed cutting technology, metal cutting principles and tools.

Wenqi Jiang received a master’s degree from Harbin University of Science and Technology. His current research focuses on high-speed cutting technology, metal cutting principles and tools.

Baowei Li received a master’s degree from Harbin University of Science and Technology. His current research focuses on high-speed cutting technology, metal cutting principles and tools.

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Cheng, Y., Zhang, X., Zhang, G. et al. Thermal deformation analysis and compensation of the direct-drive five-axis CNC milling head. J Mech Sci Technol 36, 4681–4694 (2022). https://doi.org/10.1007/s12206-022-0829-8

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  • DOI: https://doi.org/10.1007/s12206-022-0829-8

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