Global sensitivity analysis of riveting parameters based on a random sampling-high dimensional model representation


The riveting process involves numerous parameters and complex problems, such as contact phenomena and material nonlinearity; therefore, it is challenging to accurately control the deformation of riveted parts by adjusting the riveting parameters. Therefore, this paper proposes a global sensitivity analysis method to determine the effects of riveting parameters on the maximum deformation of aeronautical thin-wall structures (ATWS). Considering the correlation among variables, the riveting parameters are used as input variables and the maximum deformations of ATWS are used as the output response to establish a high-precision second-order random sampling-high dimensional model representation response function. The structure and correlative sensitivity analysis method is then used to analyze the response function, and an importance ranking of the input variables is obtained to provide guidance for designs that reduce the riveting deformation of thin-walled plates.

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The authors gratefully appreciate the support of the Natural Science Foundation of Shaanxi Province (2019JM-377), Postgraduate Tutor Guidance Ability Improvement Plan in 2019 at Northwestern Polytechnical University (2019), and Xi’an Science and Technology Innovation Platform Construction Project/Key Laboratory Construction Project (2019220614SYS021CG043).

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Junqing Yin and Feng Zhang were responsible for the design and implementation of the experimental scheme of the article. Jinyu Gu was responsible for completing the article. Junqing Yin and Feng Zhang were responsible for the revision of the article. Yongdang Chen and Feng Zhang were involved in the discussion and significantly contributed to making the final draft of the article. All the authors read and approved the final manuscript.

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Correspondence to Feng Zhang.

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Yin, J., Gu, J., Chen, Y. et al. Global sensitivity analysis of riveting parameters based on a random sampling-high dimensional model representation. Int J Adv Manuf Technol 113, 465–472 (2021).

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  • Riveting
  • Global sensitivity analysis
  • Aeronautical thin-wall structures
  • Correlation
  • Random sampling-high dimensional model