A fast way to determine temperature sensor locations in thermal error compensation

  • Zhengchun Du
  • Xiaodong Yao
  • Hongfu Hou
  • Jianguo Yang


With the improvement of machining accuracy, problems associated with the thermal deformation of the machine tool structure and the thermal error compensation technique have received more and more attention. However, the complexity of the predictive thermal model limits the application of the thermal error compensation technique. Appropriate temperature sensor locations could contribute to a better thermal model and greatly reduce the amount of time spent. This paper introduces a fast way to determine temperature sensor locations for thermal error compensation. A theoretical analysis of the heat transfer, heat exchange, and thermal deformation of a 1-D structure, i.e., a spindle, is discussed. A simulation of the heat transfers and exchange process for the spindle is performed, considering different heat flux coefficients and heat transfer coefficients. The results from the theoretical analysis and the simulation indicate that there is an optimal sensor location point on the 1-D structure and that the heat flux and transfer coefficients have little influence on the position of the optimal sensor location on the 1-D structure. The experimental results prove that optimal temperature sensor locations do exist, regarding which the temperature change and the spindle thermal deformation also have a nearly linear relationship without a time delay. Thus, a linear model can be obtained via interpolation of the experimental data. Finally, the optimal temperature sensor location method is successfully applied for the thermal error compensation of a high-speed spindle of a horizontal machining center.


Thermal error Temperature sensor Optimal position Linear model Compensation 


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Funding information

This study received funding from the National Science and Technology Major Project under grant no. 2015ZX04005001.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Zhengchun Du
    • 1
  • Xiaodong Yao
    • 1
  • Hongfu Hou
    • 1
  • Jianguo Yang
    • 1
  1. 1.School of Mechanical EngineeringShanghai Jiaotong UniversityShanghaiChina

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