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<p>High-quality aircraft assembly is crucial in modern aviation manufacturing. Assembly simulation methods are used to evaluate the assembly feasibility of components, but the accuracy of simulation models and the reliability of analysis results still need to be improved. Therefore, this paper introduces reconstruction accuracy regression based on the NURBS algorithm and adds iterative updates of the reconstruction base surface to achieve high-precision reconstruction of the simulation model. Then, based on the shape and dimensional parameters of the assembly feature simulation model, an uncertain region of the assembly pose is constructed. The Monte Carlo sampling method is used to sample the pose, generating a model of the pose uncertainty after the monitored feature is assembled, which is used to evaluate the assembly feasibility of the component. Finally, the experimental results show that the proportion of points with reconstruction accuracy within 0.005 mm is 96%, and the deviation between the expected simulation pose and the actual pose of the monitored feature is within ± 0.05, which verifies the method’s reliability.</p>

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An assembly position simulation method for aircraft virtual assembly analysis

  • 期刊论文

  • 发表日期：

    2025年9月3日

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  • Shuchun Wang,
  • Yun Peng,
  • Zheming Ma,
  • Guoyi Hou,
  • Shuanggao Li,
  • Xiang Huang

摘要

High-quality aircraft assembly is crucial in modern aviation manufacturing. Assembly simulation methods are used to evaluate the assembly feasibility of components, but the accuracy of simulation models and the reliability of analysis results still need to be improved. Therefore, this paper introduces reconstruction accuracy regression based on the NURBS algorithm and adds iterative updates of the reconstruction base surface to achieve high-precision reconstruction of the simulation model. Then, based on the shape and dimensional parameters of the assembly feature simulation model, an uncertain region of the assembly pose is constructed. The Monte Carlo sampling method is used to sample the pose, generating a model of the pose uncertainty after the monitored feature is assembled, which is used to evaluate the assembly feasibility of the component. Finally, the experimental results show that the proportion of points with reconstruction accuracy within 0.005 mm is 96%, and the deviation between the expected simulation pose and the actual pose of the monitored feature is within ± 0.05, which verifies the method’s reliability.