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作者:

Guo Tieneng (Guo Tieneng.) | Meng Lingjun (Meng Lingjun.) | Hua Xu (Hua Xu.) | Zhou Cheng (Zhou Cheng.) | Peng Liwei (Peng Liwei.)

收录:

PubMed

摘要:

Determining the weak parts of a structure is one of the key issues in the field of machine tool stiffness improvement. However, studies show that overcoming the static deformation with acquisition difficulty is a complex problem in practical structures. This study considers the machine tool cantilever structure, as a cantilever beam and bar structure, where the objective is to propose a weakness index, to identify the weak part, using system reconstruction to extract the measured static deformation data and the fitting data. Stiffness reduction is used to simulate weak parts, while the effectiveness of the method is evaluated, in the case of various weakness values and of different noise levels, using the finite element simulation approach. The validity of the proposed method is illustrated through comparison of the theoretical results to the experimental ones, using the cantilever structure of a test machine tool. The research content provides some means of improving the machining accuracy of machine tools.

关键词:

Cantilever structures experimental modal analysis finite element method static deformation weakness index

作者机构:

  • [ 1 ] [Guo Tieneng]Institute of Advanced Manufacturing and Intelligent Technology, Beijing University of Technology, Beijing, China
  • [ 2 ] [Meng Lingjun]Institute of Advanced Manufacturing and Intelligent Technology, Beijing University of Technology, Beijing, China
  • [ 3 ] [Hua Xu]Institute of Advanced Manufacturing and Intelligent Technology, Beijing University of Technology, Beijing, China
  • [ 4 ] [Zhou Cheng]Institute of Advanced Manufacturing and Intelligent Technology, Beijing University of Technology, Beijing, China
  • [ 5 ] [Peng Liwei]Institute of Advanced Manufacturing and Intelligent Technology, Beijing University of Technology, Beijing, China

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来源 :

Science progress

ISSN: 2047-7163

期: 2

卷: 104

页码: 368504211016969

2 . 1 0 0

JCR@2022

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