Advertisement

Weighted Self-regulation Complex Network-Based Modeling and Key Nodes Identification of Multistage Assembling Process

  • Peng Zhu
  • Jian-bo YuEmail author
Conference paper

Abstract

This paper proposed a weighted self-regulation variation propagation network (WSRVPN) modeling and key nodes identification method based on the complex network for multistage assembly process. Firstly, a self-regulation weighted variation transmission network is constructed through using actual machining error, quality characteristic information and assembly process requirements. Then, the weighted LeaderRank sorting algorithm is introduced to rank the importance of nodes in the network and find the key nodes. To ensure the final assembly’s quality by controlling the quality of critical nodes. The multistage assembling process of a bevel gear assembly is studied, which proves that the method can effectively model the complicated assembly deviation flow and identify the key weak points.

Keywords

Complex network Key assembling features Key nodes identification Self-regulation 

Notes

Acknowledgements

This research was supported by the National Natural Science Foundation of China (No. 51375290, 71777173), the Fundamental Research Funds for Central Universities, and Shanghai Science.

References

  1. 1.
    S.J. Hu, Y. Koren, Stream-of-variation theory for automotive body assembly. CIRP Ann. Manuf. Technol. 46(1), 1–6 (1997)CrossRefGoogle Scholar
  2. 2.
    D. Ceglarek, W. Huang, S. Zhou et al., Time-based competition in multistage manufacturing: Stream-of-variation analysis (SOVA) methodology. Int. J. Flex. Manuf. Syst. 16(1), 11–44 (2004)CrossRefGoogle Scholar
  3. 3.
    Y. Ding, J. Shi, D. Ceglarek, Diagnosability analysis of multi-station manufacturing processes. J. Dyn. Syst. Meas. Contr. 124(1), 1–13 (2002)CrossRefGoogle Scholar
  4. 4.
    J. Shi, Stream of variation modeling and analysis for multistage manufacturing processes (CRC Press, 2006)Google Scholar
  5. 5.
    J. Camelio, S.J. Hu, D. Ceglarek, Modeling variation propagation of multi-station assembly systems with compliant parts. J. Mech. Des. 125(4), 673–681 (2003)CrossRefGoogle Scholar
  6. 6.
    H. Wang, X. Ding, Identifying sources of variation in horizontal stabilizer assembly using finite element analysis and principal component analysis. Assembl. Autom. 33(1), 86–96 (2013)CrossRefGoogle Scholar
  7. 7.
    J. Liu, Variation reduction for multistage manufacturing processes: a comparison survey of statistical-process-control vs stream-of-variation methodologies. Qual. Reliab. Eng. Int. 26(7), 645–661 (2010)CrossRefGoogle Scholar
  8. 8.
    T. Zhang, J. Shi, Stream of variation modeling and analysis for compliant composite part assembly—part II: multistation processes. J. Manuf. Sci. Eng. 138(12), 121004 (2016)CrossRefGoogle Scholar
  9. 9.
    J.V. Abellan-Nebot, J. Liu, F.R. Subirn et al., State space modeling of variation propagation in multistation machining processes considering machining-induced variations. J. Manuf. Sci. Eng. 134(2), 021002 (2012)CrossRefGoogle Scholar
  10. 10.
    K.-X. Peng, L. Ma, K. Zhang, Review of quality-related fault detection and diagnosis techniques for complex industrial processes. Acta Automatica Sinica 43(3), 349–365 (2017)Google Scholar
  11. 11.
    Y. Shi, M. Gregory, International manufacturing networks—to develop global competitive capabilities. J. Oper. Manag. 16(2–3), 195–214 (1998)CrossRefGoogle Scholar
  12. 12.
    Z.H.O.U. Sheng-Xinag, Study on extraction of machining features about parts of revolution. Acta Automatica Sinica 25(6), 848–851 (1999)Google Scholar
  13. 13.
    S. Brin, L. Page, The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. ISDN Syst. 30(1) (1998)CrossRefGoogle Scholar
  14. 14.
    Q. Li, T. Zhou, L. Lü et al., Identifying influential spreaders by weighted LeaderRank. Physica A 404, 47–55 (2014)CrossRefGoogle Scholar
  15. 15.
    S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, D.U. Hwang, Complex networks: structure and dynamics. Phys. Rep. 424(4), 175–308 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Mechanical EngineeringTongji UniversityShanghaiChina

Personalised recommendations