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Robust machine tool thermal error compensation modelling based on temperature-sensitive interval segmentation modelling technology

  • Yunsheng Liu
  • Enming MiaoEmail author
  • Hui Liu
  • Yangyang Chen
ORIGINAL ARTICLE
  • 13 Downloads

Abstract

Establishing a mathematical model that can reflect the relationship between temperature increase and thermal error during machining is the core of thermal error compensation technology for CNC machine tools. The collinearity between temperature-sensitive points and the correlation between temperature-sensitive points and thermal errors are important factors affecting the prediction accuracy and robustness of the thermal error compensation model. Based on the thermal error measurement experiments of the Leaderway-V450 CNC machine tool in different periods of the year, the principal component regression (PCR) modelling algorithm, which can eliminate the collinearity effect, is proposed to establish the thermal error compensation model of the machine tool on the basis of selecting the temperature-sensitive points by using the correlation coefficient. It is compared with the newly proposed ridge regression thermal error compensation modelling algorithm. The results show that the thermal error compensation modelling method proposed in this paper can basically control the Z-direction thermal error of the CNC machine tool spindle within 10 μm with only two temperature sensors and has higher engineering practicability. It is found that the thermal error compensation model of machine tools has a jump interval affected by the ambient temperature. This interval is called the temperature-sensitive interval, and a temperature-sensitive interval subsection point selection algorithm is proposed to build a subsection model on both sides of the segment point. The results show that the Z-direction thermal error of the spindle of CNC machine tools can be basically controlled within 5 μm with only two temperature sensors and that the model is highly robust and has great engineering application value.

Keywords

CNC machine tool Thermal error Collinearity Principal component regression algorithm Temperature-sensitive interval Segmentation modelling 

Notes

Acknowledgements

This work is supported by the Key Project of the National Natural Science Foundation of China (No. 51490660/51490661) and the Scientific Research Foundation of Chongqing University of Technology.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Yunsheng Liu
    • 1
  • Enming Miao
    • 1
    Email author
  • Hui Liu
    • 2
  • Yangyang Chen
    • 1
  1. 1.College of Mechanical EngineeringChongqing University of TechnologyChongqingChina
  2. 2.School of AutomationXian University of Posts & TelecommunicationsXianChina

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