Advertisement

An Ensemble Modeling for Thermal Error of CNC Machine Tools

  • Xuemei Jiang
  • PanPan Zhu
  • Ping LouEmail author
  • Xiaomei Zhang
  • Quan Liu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 946)

Abstract

Thermal error caused by the thermal deformation of computer numerical control (CNC) machine tools is one of the main factors to affect the machining accuracy. With monitoring data of the temperature field, establishing data-driven thermal error model is considered as a more convenient, effective and cost-efficient way to reduce the thermal error. As a matter of fact, it is very difficult to develop a thermal error model with perfect generalization adapting to different working conditions of machining tools. In this paper, a method of an ensemble modeling (EM) based on Convolution Neural Network (CNN) and Back Propagation (BP) Neural Network for modeling thermal error is presented. This ensemble model takes full advantages of two different neural networks, namely CNN having self-extracting feature to solve collinear problem in temperature field and BP can process heat source to thermal error by mapping nonlinear function, then combined into a EM. To demonstrate the effectiveness of the proposed model, an experiment platform was set up based on a heavy-duty CNC machine tool. The results show that the proposed model achieves better accuracy and strong robustness in comparison with only with BP network and CNN network respectively.

Keywords

Thermal error Ensemble modeling CNC machine Convolution neural network Back propagation network 

Notes

Acknowledgement

The authors would like to acknowledge funding support from the National Natural Science Foundation Committee of China under Grant No. 51475347 and the Major Project of Technological Innovation Special Fund of Hubei Province Grant No. 2016AAA016, as well as the contributions from all collaborators within the projects mentioned. We would also like to thank Wuhan University of Technology, People’s Republic of China in supporting this work.

References

  1. 1.
    Ramesh, R., Mannan, M.A., Poo, A.N.: Error compensation in machine tools—a review: Part II: thermal errors. Int. J. Mach. Tools Manuf. 40(9), 1257–1284 (2000)CrossRefGoogle Scholar
  2. 2.
    Denkena, B., Schmidt, C., Krüger, M.: Experimental investigation and modeling of thermal and mechanical influences on shape deviations in machining structural parts. Int. J. Mach. Tools Manuf. 50(11), 1015–1021 (2010)CrossRefGoogle Scholar
  3. 3.
    Postlethwaite, S.R., Allen, J.P., Ford, D.G.: Machine tool thermal error reduction—an appraisal. Proc. Inst. Mech. Eng. 213(213), 1–9 (1999)Google Scholar
  4. 4.
    Section I.: An adaptive finite element method for stationary incompressible thermal flow based on projection error estimation. Math. Prob. Eng. 2013(2), 1–14 (2013)Google Scholar
  5. 5.
    Kim, J., Zverv, I., Lee, K.: Thermal model of high-speed spindle units. Intell. Inf. Manag. 02(05), 306–315 (2010)Google Scholar
  6. 6.
    Li, Y., et al.: A review on spindle thermal error compensation in machine tools. Int. J. Mach. Tools Manuf. 95, 20–38 (2015)CrossRefGoogle Scholar
  7. 7.
    Li, Y., Zhao, W., Wu, W., et al.: Thermal error modeling of the spindle based on multiple variables for the precision machine tool. Int. J. Adv. Manuf. Technol. 72(9–12), 1415–1427 (2014)CrossRefGoogle Scholar
  8. 8.
    Pahk, H.J., Lee, S.W.: Thermal error measurement and real time compensation system for the CNC machine tools incorporating the spindle thermal error and the feed axis thermal error. In: Hayhurst, D.R. (ed.) Proceedings of the 33rd International Conference, pp. 249–254. Springer, Heidelberg (2000).  https://doi.org/10.1007/978-1-4471-0777-4_39CrossRefGoogle Scholar
  9. 9.
    Ruijun, L., Wenhua, Y., Zhang, H.H., Qifan, Y.: The thermal error optimization models for CNC machine tools. Int. J. Adv. Manuf. Technol. 63(9–12), 1167–1176 (2012)CrossRefGoogle Scholar
  10. 10.
    Baltagi, B.H.: Multiple Regression Analysis. In: Baltagi, B.H. (ed.) Econometrics. Springer, Heidelberg (2002).  https://doi.org/10.1007/978-3-662-04693-7_4CrossRefzbMATHGoogle Scholar
  11. 11.
    Ren, X., Sun, Y., Zhou, T., Xu, W., Yue, Y.: Real-time thermal error compensation on machine tools using improved BP neural network. In: 2011 International Conference on Electric Information and Control Engineering, Wuhan, pp. 630–632 (2011)Google Scholar
  12. 12.
    Wang, J., Qin, B., Liu, Y., Yang, Y.: Thermal error prediction of numerical control machine based on improved particle swarm optimized back propagation neural network. In: 11th International Conference on Natural Computation (ICNC), Zhangjiajie, pp. 820–824 (2015)Google Scholar
  13. 13.
    Wang, P., Jin, Z.F., Zheng, Y.l.: Artificial neural network-based thermal error model-ling in ball screw. In: IEEE Symposium on Electrical & Electronics Engineering (EEESYM), Kuala Lumpur, pp. 67–70 (2012)Google Scholar
  14. 14.
    Ren, B., Ren, X., Huang, S., Li, G.: The research on thermal error modeling and compensation on machine tools. In: International Conference on Control Engineering and Communication Technology, Liaoning, pp. 444–447 (2012)Google Scholar
  15. 15.
    Pani, A.K., Mohanta, H.K.: A hybrid soft sensing approach of a cement mill using principal component analysis and artificial neural networks. In: 3rd IEEE International Advance Computing Conference (IACC), Ghaziabad, pp. 713–718 (2013)Google Scholar
  16. 16.
    Zhao, C., Wang, Y.: Optimization of measuring points based on the grey system theory for spindle of CNC machine tool. In: International Conference on Mechatronics and Automation, Changchun, pp. 686–690 (2009)Google Scholar
  17. 17.
    Yao, X.H., Fu, J.Z., Chen, Z.C.: Bayesian networks modeling for thermal error of numerical control machine tools. J. Zhejiang Univ.-Sci. (Appl. Phys. Eng.) 9(11), 1524–1530 (2008)CrossRefGoogle Scholar
  18. 18.
    L’Heureux, A., Grolinger, K., Elyamany, H.F., et al.: Machine learning with big data: challenges and approaches. IEEE Access 5(99), 7776–7797 (2017)CrossRefGoogle Scholar
  19. 19.
    Yan, J., Meng, Y., Lu, L., Guo, C.: Big-data-driven based intelligent prognostics scheme in industry 4.0 environment. In: Prognostics and System Health Management Conference (PHM-Harbin), Harbin, pp. 1–5 (2017)Google Scholar
  20. 20.
    Hansen, L.K., Salamon, P.: Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 12(10), 993–1001 (2002)CrossRefGoogle Scholar
  21. 21.
    Huang, Y., Zhang, J., Li, X., et al.: Thermal error modeling by integrating GA and BP algorithms for the high-speed spindle. Int. J. Adv. Manuf. Technol. 71(9–12), 1669–1675 (2014)CrossRefGoogle Scholar
  22. 22.
    SU, Y.-F., Yuan, W.X., Liu, D.P., et al.: A thermal errors compensation model for high-speed motorized spindle based on bp neural network. Modul. Mach. Tool Autom. Manuf. Tech. (2013)Google Scholar
  23. 23.
    Yang, H., Ni, J.: Dynamic neural network modeling for nonlinear, nonstationary machine tool thermally induced error. Int. J. Mach. Tools Manuf. 45(4), 455–465 (2005)CrossRefGoogle Scholar
  24. 24.
    Zhang, Y., Yang, J., Jiang, H.: Machine tool thermal error modeling and prediction by grey neural network. Int. J. Adv. Manuf. Technol. 59(9–12), 1065–1072 (2012)CrossRefGoogle Scholar
  25. 25.
    Han, J., Wang, H., Cheng, N.: A new thermal error modeling method for CNC machine tools. Int. J. Adv. Manuf. Technol. 62(1–4), 205–212 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Information EngineeringWuhan University of TechnologyWuhanChina

Personalised recommendations