Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 240300123-8.doi: 10.11896/jsjkx.240300123

• Interdiscipline & Application • Previous Articles     Next Articles

Study on Analysis and Prediction Method of Small Sample Aircraft Production QualityDeviation Data

WANG Luhang1, ZHANG Dongdong2, LU Hu3, LI Rupeng3, GE Xiaoli3   

  1. 1 School of Mathematical Sciences,Tongji University,Shanghai 200092,China
    2 College of Electronic and Information Engineering,Tongji University,Shanghai 201804,China
    3 Aviation Manufacturing Technology Research Institute,Shanghai Aircraft Manufacturing Co.,Ltd.,Shanghai 201324,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:WANG Luhang,born in 2002,undergraduate.His main research interest is aircraft production quality control.
    ZHANG Dongdong,born in 1977,Ph.D,professor,Ph.D supervisor.Her main research interests include image processing and deep learning.
  • Supported by:
    National Key R&D Program of China(2021YFB3301901).

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Abstract: With the development of modern industrial capabilities and the demand for increased precision in aircraft,the analysis and control of aircraft production quality have become a focal point for major aerospace enterprises.At the current stage,traditional analytical methods face challenges in accurately constructing deviation analysis models for aircraft production due to inhe-rent features such as limited reference sample data,significant uncertainties,non-linearity,and multi-level assembly deviations.Therefore,this paper focuses on the deviations in the aircraft production process and systematically explores methods for analyzing and predicting aircraft production quality discrepancies.Firstly,this paper analyzes the deviation relationships among various components,identifying the key component with the greatest impact on total deviation based on principal component analysis,which pinpoints the focus for predictive targeting.Subsequently,starting from actual production data resembling a normal distribution,this paper pays special attention to key components,enabling the prediction,generation,and validation of deviation data based on the normal cloud model,which yields aircraft production quality deviation data and their memberships for a greater varietyof samples,alleviating the issue of “small sample” and evaluating the predictive model through k-fold cross-validation.Finally,a cooperative predictive model for assembly deviation fluctuation intervals based on the improved grey forecasting model and multi-source data fusion is established.The alleviation of the “small sample” issue enhances the precision and scientific nature of interval prediction.With reference to tolerance data,predict the interval range where aircraft production quality deviations are located,providing guidance for actual production and the formulation of tolerance correction mechanisms.

Key words: Small sample data, Aircraft production quality control, Mathematical modeling, Principal component analysis, Normal cloud model, K-fold cross validation, Grey forecasting model

CLC Number: 

  • TP399
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