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作者:

Niu, Peng (Niu, Peng.) | Cheng, Qiang (Cheng, Qiang.) (学者:程强) | Liu, Zhifeng (Liu, Zhifeng.) (学者:刘志峰) | Chu, Hongyan (Chu, Hongyan.)

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EI Scopus SCIE

摘要:

In the actual production, it is expected to find several sensitivity geometric errors which have great influence on machining accuracy, so as to provide references for the manufacturing, assembly, and other links of the machine tool, and fundamentally improve the working performance. To study this problem, a novel global sensitivity analysis (GSA) method is proposed. Based on the MBS theory, the spatial error model is established to analyze the local influence of geometric error on machining accuracy. An improved second-order partial correlation coefficient based on the Pearson product moment is proposed to analyze the correlation between geometric errors. In addition, the error parameters will change greatly in the stroke of the moving parts. However, the rapid fluctuation of geometric error value will have a dynamic impact on the machining accuracy of machine tools, which is rarely noticed in the previous sensitivity analysis. This change is defined as the fluctuation of error. The high fitting degree function of error-displacement is obtained by using the high order Fourier series and sine series. Then, the fluctuation of the error in the machining stroke is analyzed by using the derivative of the function to the displacement. Through geometric error characteristics (including the local influence, correlation, and fluctuation) are studied comprehensively, the GSA of error is carried out. Finally, taking a machining center as an example and combining the sensitivity analysis results, the improvement measures are proposed to verify the correctness of the method. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature.

关键词:

Machining centers Fourier series Machining Sensitivity analysis Errors Geometry Manufacture

作者机构:

  • [ 1 ] [Niu, Peng]Institute of Advanced Manufacturing and Intelligent Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Niu, Peng]Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Cheng, Qiang]Institute of Advanced Manufacturing and Intelligent Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Cheng, Qiang]Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 5 ] [Liu, Zhifeng]Institute of Advanced Manufacturing and Intelligent Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 6 ] [Liu, Zhifeng]Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 7 ] [Chu, Hongyan]Institute of Advanced Manufacturing and Intelligent Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 8 ] [Chu, Hongyan]Machinery Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing Technology, Beijing University of Technology, Beijing; 100124, China

通讯作者信息:

  • 程强

    [cheng, qiang]institute of advanced manufacturing and intelligent technology, beijing university of technology, beijing; 100124, china;;[cheng, qiang]beijing key laboratory of advanced manufacturing technology, beijing university of technology, beijing; 100124, china

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来源 :

International Journal of Advanced Manufacturing Technology

ISSN: 0268-3768

年份: 2021

期: 9-10

卷: 112

页码: 2873-2887

3 . 4 0 0

JCR@2022

ESI学科: ENGINEERING;

ESI高被引阀值:87

JCR分区:2

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 50

ESI高被引论文在榜: 0 展开所有

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