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.
Complex network Key assembling features Key nodes identification Self-regulation
This is a preview of subscription content, log in to check access.
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.
S.J. Hu, Y. Koren, Stream-of-variation theory for automotive body assembly. CIRP Ann. Manuf. Technol. 46(1), 1–6 (1997)CrossRefGoogle Scholar
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
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
J. Shi, Stream of variation modeling and analysis for multistage manufacturing processes (CRC Press, 2006)Google Scholar
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
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
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
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
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
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
Y. Shi, M. Gregory, International manufacturing networks—to develop global competitive capabilities. J. Oper. Manag. 16(2–3), 195–214 (1998)CrossRefGoogle Scholar
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
S. Brin, L. Page, The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. ISDN Syst. 30(1) (1998)CrossRefGoogle Scholar
Q. Li, T. Zhou, L. Lü et al., Identifying influential spreaders by weighted LeaderRank. Physica A 404, 47–55 (2014)CrossRefGoogle Scholar
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