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作者:

Chen, Dongju (Chen, Dongju.) (学者:陈东菊) | Li, Yuan (Li, Yuan.) | Zha, Chunqing (Zha, Chunqing.) | Pan, Ri (Pan, Ri.) | Fan, Jinwei (Fan, Jinwei.) (学者:范晋伟)

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EI CSCD

摘要:

In view of lack of analysis and optimization of existing hydrostatic spindle motion errors, a dynamic model of the spindle motion error is constructed based on structural parameters, and the laws of dynamic parameters of the spindle rotor at different rotational speeds with an unbalanced mass are quantitatively analyzed. The mathematical model of the spindle motion error is taken as the objective function, and the spindle system parameters are used as design variables. Genetic algorithm is adopted to optimize the motion error of the hydrostatic spindle system. In the single-factor optimization analysis, the optimization efficiency for the radial error motions x and y of the spindle system and the spindle inclination angle θ reaches 41.22%, 25.21% and 66.16%, respectively, and the efficiency gets 4.7% during multi-objective optimization. The optimized spindle motion accuracy is significantly improved, and a combination of structural parameters is obtained to heighten the hydraulic static pressure spindle motion accuracy. © 2020, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.

关键词:

Efficiency Errors Functions Genetic algorithms Hydraulics Multiobjective optimization

作者机构:

  • [ 1 ] [Chen, Dongju]Mechanical Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Li, Yuan]Mechanical Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Zha, Chunqing]Mechanical Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Pan, Ri]Mechanical Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing, Beijing University of Technology, Beijing; 100124, China
  • [ 5 ] [Fan, Jinwei]Mechanical Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing, Beijing University of Technology, Beijing; 100124, China

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来源 :

Journal of Xi'an Jiaotong University

ISSN: 0253-987X

年份: 2020

期: 6

卷: 54

页码: 90-98

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SCOPUS被引频次: 2

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