Chinese Journal of Mechanical Engineering >

2022 , Vol. 35 >Issue 5: 105 - 105

DOI: https://doi.org/10.1186/s10033-022-00763-8

Intelligent Manufacturing Technology

Digital Twin-based Quality Management Method for the Assembly Process of Aerospace Products with the Grey-Markov Model and Apriori Algorithm

  • Cunbo Zhuang ,
  • Ziwen Liu ,
  • Jianhua Liu ,
  • Hailong Ma ,
  • Sikuan Zhai ,
  • Ying Wu
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  • 1. Laboratory of Digital Manufacturing, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China;
    2. Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing, 314000, China;
    3. Shanghai Institute of Spacecraft Equipment, Shanghai, 200240, China

收稿日期: 2021-11-16

  修回日期: 2022-05-10

  网络出版日期: 2023-04-24

基金资助

Supported by National Key Research and Development Program of China (Grant No. 2020YFB1710300), National Natural Science Foundation of China (Grant No. 52005042), National Defense Fundamental Research Foundation of China (Grant No. JCKY2020203B039), Equipment Pre-research Foundation of China (Grant No. 80923010101), and Beijing Institute of Technology Research Fund Program for Young Scholars.

收起

Digital Twin-based Quality Management Method for the Assembly Process of Aerospace Products with the Grey-Markov Model and Apriori Algorithm

  • Cunbo Zhuang ,
  • Ziwen Liu ,
  • Jianhua Liu ,
  • Hailong Ma ,
  • Sikuan Zhai ,
  • Ying Wu
Expand
  • 1. Laboratory of Digital Manufacturing, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China;
    2. Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing, 314000, China;
    3. Shanghai Institute of Spacecraft Equipment, Shanghai, 200240, China

Received date: 2021-11-16

  Revised date: 2022-05-10

  Online published: 2023-04-24

Supported by

Supported by National Key Research and Development Program of China (Grant No. 2020YFB1710300), National Natural Science Foundation of China (Grant No. 52005042), National Defense Fundamental Research Foundation of China (Grant No. JCKY2020203B039), Equipment Pre-research Foundation of China (Grant No. 80923010101), and Beijing Institute of Technology Research Fund Program for Young Scholars.

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摘要

The assembly process of aerospace products such as satellites and rockets has the characteristics of single- or small-batch production, a long development period, high reliability, and frequent disturbances. How to predict and avoid quality abnormalities, quickly locate their causes, and improve product assembly quality and efficiency are urgent engineering issues. As the core technology to realize the integration of virtual and physical space, digital twin (DT) technology can make full use of the low cost, high efficiency, and predictable advantages of digital space to provide a feasible solution to such problems. Hence, a quality management method for the assembly process of aerospace products based on DT is proposed. Given that traditional quality control methods for the assembly process of aerospace products are mostly post-inspection, the Grey-Markov model and T-K control chart are used with a small sample of assembly quality data to predict the value of quality data and the status of an assembly system. The Apriori algorithm is applied to mine the strong association rules related to quality data anomalies and uncontrolled assembly systems so as to solve the issue that the causes of abnormal quality are complicated and difficult to trace. The implementation of the proposed approach is described, taking the collected centroid data of an aerospace product’s cabin, one of the key quality data in the assembly process of aerospace products, as an example. A DT-based quality management system for the assembly process of aerospace products is developed, which can effectively improve the efficiency of quality management for the assembly process of aerospace products and reduce quality abnormalities.

关键词： Quality management; Digital twin; Assembly process; Aerospace product; Grey Markov model; Apriori algorithm

本文引用格式

Cunbo Zhuang , Ziwen Liu , Jianhua Liu , Hailong Ma , Sikuan Zhai , Ying Wu . Digital Twin-based Quality Management Method for the Assembly Process of Aerospace Products with the Grey-Markov Model and Apriori Algorithm[J]. Chinese Journal of Mechanical Engineering, 2022 , 35(5) : 105 -105 . DOI: 10.1186/s10033-022-00763-8

Abstract

The assembly process of aerospace products such as satellites and rockets has the characteristics of single- or small-batch production, a long development period, high reliability, and frequent disturbances. How to predict and avoid quality abnormalities, quickly locate their causes, and improve product assembly quality and efficiency are urgent engineering issues. As the core technology to realize the integration of virtual and physical space, digital twin (DT) technology can make full use of the low cost, high efficiency, and predictable advantages of digital space to provide a feasible solution to such problems. Hence, a quality management method for the assembly process of aerospace products based on DT is proposed. Given that traditional quality control methods for the assembly process of aerospace products are mostly post-inspection, the Grey-Markov model and T-K control chart are used with a small sample of assembly quality data to predict the value of quality data and the status of an assembly system. The Apriori algorithm is applied to mine the strong association rules related to quality data anomalies and uncontrolled assembly systems so as to solve the issue that the causes of abnormal quality are complicated and difficult to trace. The implementation of the proposed approach is described, taking the collected centroid data of an aerospace product’s cabin, one of the key quality data in the assembly process of aerospace products, as an example. A DT-based quality management system for the assembly process of aerospace products is developed, which can effectively improve the efficiency of quality management for the assembly process of aerospace products and reduce quality abnormalities.

Key words： Quality management; Digital twin; Assembly process; Aerospace product; Grey Markov model; Apriori algorithm

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