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Error Propagation Model Using Jacobian-Torsor Model Weighting for Assembly Quality Analysis on Complex Product

Author

Listed:
  • Yue Xi

    (State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Zhiyong Gao

    (State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Kun Chen

    (State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Hongwei Dai

    (State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Zhe Liu

    (State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

The assembly quality of a complex product is the result of the combined effects of multiple manufacturing stages, including design, machining and assembly, and it is influenced by associated elements with complex coupling mechanisms. These elements generate and transmit assembly quality deviations during the assembly process which are difficult to analyze and express effectively. Current studies have focused on the analysis and optimization of the assembly surface errors of single or few components, while lacking attention to the impact of errors on the whole product. Therefore, in order to solve the above problem, an assembly quality deviation analysis (AQDA) model is constructed in this paper to analyze the deviation transfer process in the assembly process of complex products and to obtain the key features to optimize. Firstly, the assembly process information is extracted and the assembly quality network model is established on the basis of complex networks. Second, the Jacobian-Torsor (J-T) model is introduced to form a network edge weighting method suitable for the assembly process to objectively express the error propagation among product part features. Third, an error propagation model (EPM) is designed to simulate the error propagation and diffusion processes in the assembly network. Finally, the assembly process of an aero-engine fan rotor is used as an example for modeling and analysis. The results show that the proposed method can effectively identify the key assembly features in the assembly process of complex products and determine the key quality optimization points and monitoring points of the products, which can provide a decision basis for product quality optimization and control.

Suggested Citation

  • Yue Xi & Zhiyong Gao & Kun Chen & Hongwei Dai & Zhe Liu, 2022. "Error Propagation Model Using Jacobian-Torsor Model Weighting for Assembly Quality Analysis on Complex Product," Mathematics, MDPI, vol. 10(19), pages 1-18, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3534-:d:928143
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    References listed on IDEAS

    as
    1. Ran Jin & Jianjun Shi, 2012. "Reconfigured piecewise linear regression tree for multistage manufacturing process control," IISE Transactions, Taylor & Francis Journals, vol. 44(4), pages 249-261.
    2. Yupeng Li & Zhaotong Wang & Xiaoyu Zhong & Fan Zou, 2019. "Identification of influential function modules within complex products and systems based on weighted and directed complex networks," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2375-2390, August.
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