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Thermal error modeling of the spindle based on multiple variables for the precision machine tool

  • Yang Li
  • Wanhua ZhaoEmail author
  • Wenwu Wu
  • Bingheng Lu
  • Yubao Chen
ORIGINAL ARTICLE

Abstract

Thermal error, especially the one caused by the thermal expansion of spindle in axial direction, seriously impacts the accuracy of the precision machine tool. Thermal error compensation based on the thermal error model with high accuracy and robustness is an effective and economic way to reduce the impact and enhance the accuracy. Generally, thermal error models are built only on temperatures at some points in the spindle system. However, the thermal error is also closely related to other working parameters. Through the theoretical analysis, the simulation, and the experimental testing in this paper, it is found out that thermal error is determined by multiple variables, such as the temperature, the spindle rotation speed, the historical spindle temperature, the historical thermal error, and the time lag between the present and previous times. In order to examine the performance of thermal error models based on multiple variables, two common methods are used for modeling—the multiple regression method and the back propagation network. The data for modeling are collected from experiments conducted on the spindle of a precision machine tool under various working conditions. The modeling results demonstrate that models established based on the multiple variables have better accuracy and robustness. It also turns out that data filtering before modeling can further improve the performance of the models. Therefore, models based on multiple variables with good accuracy and robustness can be very useful for the further thermal error compensation. In addition, by taking relative importance analysis of multiple variables based on standardized regression coefficients, the influence of each variable to the thermal error is revealed. The ranking of coefficients can also be used as a new criterion for the optimal temperature variable selection in the future research.

Keywords

Spindle thermal error modeling Multiple variables Multiple regression model Back propagation network model Standardized regression coefficients 

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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Yang Li
    • 1
  • Wanhua Zhao
    • 1
    • 3
    Email author
  • Wenwu Wu
    • 1
  • Bingheng Lu
    • 1
  • Yubao Chen
    • 2
  1. 1.State Key Laboratory for Manufacturing System EngineeringXi’an Jiaotong UniversityXi’anChina
  2. 2.Department of Industrial and Manufacturing Systems EngineeringUniversity of Michigan–DearbornDearbornUSA
  3. 3.School of Mechanical EngineeringXi’an Jiaotong UniversityXi’anChina

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