Comparison of Forecasting Methods for Thermal Error on High-Speed Motorized Spindle

Abstract:

In order to reduce the thermal error of the motorized spindle and improve the manufacturing accuracy of NC machine tool, the thermal error forecasting models based on multivariate autoregressive (MVAR) method and genetic radial basis function (GARBF) neural network method are proposed, respectively. According to different representations of generation mechanism of motorized spindle thermal deformation, operation efficiency and curve fit precision of these two models are compared. The studied results show that under the same temperature rise variable conditions, MVAR model and GARBF neural network model have almost the same convergence and operation time and relative errors of two models are less than 3%. The results also show that the MVAR model has higher forecast precision in the prediction former stages; in contrast, the GARBF neural network model has higher forecast precision in the latter stages.

• Periodical: Advanced Materials Research (Volumes 291-294)
• Pages: 2991-2994
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.291-294.2991
• Online since: July 2011
• Authors: Chun Li Lei, Zhi Yuan Rui, Jun Liu, Jing Fang Fang
• Keywords: Artificial Neural Network (ANN), Forecasting, Motorized Spindle, Multivariate Autoregressive Method, Thermal Error

References

[1] J.B. Bryan: Ann CIRP, Vol. 39 (1990), p.645

[2] J. Ni: J Manuf. Sci. Eng. Vol.119 (1997), p.717

[3] J. Zhu: Robust thermal error modeling and compensation for CNC machine tools[D]:[Doctor of Philosophy]. The University Of Michigan, (2008)

[4] J.S. Chen, J.X. Yuan, J. Ni, et al: J. Eng. Indus. Vol. 115 (1993), p.472

[5] M.A. Donmez, D.S. Blomquist, R.J. Hocken, et al: Prec. Eng. Vol. 8 (1986), p.187

[6] J.H. Lee, J.H. Lee, S.H. Yang: J. Manuf. Proc. Vol. 3 (2001), p.120

[7] C.L. Lei, Z.Y. Rui: Adv. Mater. Res. Vol. 129-131 (2010), p.556

[8] Z. X. Guo, W. K. Wong, S. Y. S. Leung, et al: Int. J. Adv. Manuf. Tech. Vol. 36 (2008), p.156

[9] E. Salajegheh, S. Gholizadeh: Adv. Eng. Softw. Vol. 36 (2005), p.757

[10] J. Yang, J. Yuan, J. Ni: Int. J. Mach. Tools Manuf. Vol. 39 (1999), p.1367

[11] W. Y. Wang: Mech. Syst. Signal Pr. Vol.15(2001), p.887