Structural Engineering and Mechanics

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A hybrid method for dynamic stiffness identification of bearing joint of high speed spindles

  • Zhao, Yongsheng (Key Laboratory of advanced manufacturing technology, Beijing University of Technology) ;
  • Zhang, Bingbing (Key Laboratory of advanced manufacturing technology, Beijing University of Technology) ;
  • An, Guoping (Key Laboratory of advanced manufacturing technology, Beijing University of Technology) ;
  • Liu, Zhifeng (Key Laboratory of advanced manufacturing technology, Beijing University of Technology) ;
  • Cai, Ligang (Key Laboratory of advanced manufacturing technology, Beijing University of Technology)
  • Received : 2014.01.01
  • Accepted : 2015.12.15
  • Published : 2016.01.10
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Abstract

Bearing joint dynamic parameter identification is crucial in modeling the high speed spindles for machining centers used to predict the stability and natural frequencies of high speed spindles. In this paper, a hybrid method is proposed to identify the dynamic stiffness of bearing joint for the high speed spindles. The hybrid method refers to the analytical approach and experimental method. The support stiffness of spindle shaft can be obtained by adopting receptance coupling substructure analysis method, which consists of series connected bearing and joint stiffness. The bearing stiffness is calculated based on the Hertz contact theory. According to the proposed series stiffness equation, the stiffness of bearing joint can be separated from the composite stiffness. Then, one can obtain the bearing joint stiffness fitting formulas and its variation law under different preload. An experimental set-up with variable preload spindle is developed and the experiment is provided for the validation of presented bearing joint stiffness identification method. The results show that the bearing joint significantly cuts down the support stiffness of the spindles, which can seriously affects the dynamic characteristic of the high speed spindles.

Keywords

Acknowledgement

Supported by : Beijing Natural Science Foundation, National Natural Science Foundation of China

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