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Key deviation source diagnosis of complex thin-walled structures based on complex networks and weighted transfer entropy

Published online by Cambridge University Press:  14 August 2023

Y.G. Zhu Open the ORCID record for Y.G. Zhu [Opens in a new window] ,
Q. Shi Open the ORCID record for Q. Shi [Opens in a new window] ,
W.P. Jiang  and
B. Deng
Y.G. Zhu*
Affiliation:
School of Aeronautical Manufacturing Engineering, Nanchang HangKong University, Nanchang, China
Q. Shi
Affiliation:
School of Aviation Electromechanical Equipment Maintenance, Changsha Aeronautical Vocational and Technical, Changsha, China
W.P. Jiang
Affiliation:
School of Aeronautical Manufacturing Engineering, Nanchang HangKong University, Nanchang, China
B. Deng
Affiliation:
Huaguan Technology Co., Ltd., Changsha, China
*
Corresponding author: Y.G. Zhu; Email: zhuyongguo_2003@163.com
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Abstract

There are many deviation sources in the assembly process of aircraft complex thin-walled structures. To get important factors that affect quality, it is crucial to diagnose the key deviation resources. The deviation transfer between deviation sources and assembly parts has the characteristics of small sample size, nonlinearity, and strong coupling, so it is difficult to diagnose the key deviation sources by constructing assembly dimension chains. Therefore, based on the deviation detection data, transfer entropy and complex network theory are introduced. Integrating the depth-first traversal algorithm with degree centrality theory, a key deviation diagnosis method for complex thin-walled structures is proposed based on weighted transfer entropy and complex networks. The application shows that key deviation sources that affect assembly quality can be accurately identified by the key deviation source diagnosis method based on complex networks and weighted transfer entropy.

Keywords

assemblydeviationthin-walled structurescomplex networkstransfer entropy
Type
Research Article
Information
The Aeronautical Journal , Volume 128 , Issue 1322 , April 2024 , pp. 812 - 829
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Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

Zhang, W., Wang, Z.G., Tan, C.B., et al. Assembly variation optimization method of aircraft compliant parts based on active locating compensation of fixture, Acta Aeronaut. Astronaut. Sin., 2017, 38, (06), pp 263–271. https://doi.org/10.7527/S1000-6893.2016.420862 Google Scholar
Bai, Q., Tian, W., Shi, Z.H., et al. Flexible assembly tooling structure and positioning accuracy analysis of aircraft fuselage framework, IOP Conf. Ser.: Mater. Sci. Eng., 2019, 470, (1), pp 216–224. https://doi.org/10.1088/1757-899X/470/1/012025 CrossRefGoogle Scholar
Liu, X., An, L.L., Wang, Z.G., et al. Assembly variation analysis of aircraft panels under part-to-part locating scheme, Int. J. Aerosp. Eng., 2019, 2019, pp 1–15. https://doi.org/10.1155/2019/9563596 Google Scholar
Chase, K.W., Gao, J.S. and Magleby, S.P. General 2-D tolerance analysis of mechanical assemblies with small kinematic adjustments, J. Des. Manufact., 1995, 5, pp 263–274.Google Scholar
Chase, K.W., Magleby, S.P. and Gao, J. Tolerance analysis of 2-D and 3-D mechanical assemblies with small kinematic adjustments. In Advanced Tolerancing Techniques. New York: John Wiley & Sons, 1997, pp 103–137.Google Scholar
Mantripragada, R. and Whitney, D.E. Modeling and controlling variation propagation in mechanical assemblies using state transition models, IEEE Trans. Robot. Automat., 1999, 1, (115), pp 124–140. https://doi.org/10.1109/70.744608 CrossRefGoogle Scholar
Zhang, H.B., Wang, M.W., Deng, W., et al. Identification of key factors and mining of association relations in complex product assembly process, Int. J. Aerosp. Eng., 2022, 2020. https://doi.org/10.1155/2022/2583437 CrossRefGoogle Scholar
Mckenna, V., Jin, Y., Murphy, A., et al. Cost-oriented process optimisation through variation propagation management for aircraft wing spar assembly, Robot. Comput.-Integr. Manufact., 2019, 57, pp 435–451. https://doi.org/10.1016/j.rcim.2018.12.009 CrossRefGoogle Scholar
Falgarone, H., Thiébaut, F., Coloos, J., et al. Variation simulation during assembly of non-rigid components, realistic assembly simulation with ANATOLEFLEX software, Proc. CIRP, 2016, 43, pp 202–207. https://doi.org/10.1016/j.procir.2016.02.336 CrossRefGoogle Scholar
Zhang, K.F., Cheng, H. and Li, Y. Riveting process modeling and simulating for deformation analysis of aircraft’s thin-walled sheet-metal parts, Chin. J. Aeronaut., 2011, (3), pp 370–375. https://doi.org/10.1016/S1000-9361(11)60044-7 Google Scholar
Qi, Z.C., Zhang, K.F., Li, Y., et al. Analysis and optimization for locating errors of large wing panel during automatic drilling and riveting, Acta Aeronaut. Astronaut. Sin., 2015, 36, 10, pp 3439–3449. https://doi.org/10.7527/S1000-6893.2015.0221 Google Scholar
Aman, F., Cheraghi, H.S., Krishna, K., et al. Study of the impact of riveting sequence, rivet pitch, and gap between sheets on the quality of riveted lap joints using finite element method, Int. J. Adv. Manufact. Technol., 2013, 67, pp 545–562. https://doi.org/10.1007/s00170-012-4504-6 CrossRefGoogle Scholar
Chen, H., Tan, C.B. and Wang, Z.G. Statistical variation analysis of compliant assembly coupling geometrical and material error, Acta Aeronaut. Astronaut. Sin., 2015, 36, (09), pp 3176–3186. https://doi.org/10.7527/S1000-6893.2014.0306 Google Scholar
Liu, S.L., Luo, Z.G., Tan, G.S., et al. 3DMeasurement and quality evaluation for complex aircraft assemblies, Acta Aeronaut. Astronaut. Sin., 2013, 34, (2), pp 409–418. https://doi.org/10.7527/S1000-6893.2013.0047 Google Scholar
Shi, Z.H., He, X.X., Zeng, D.B., et al. Error compensation method for mobile robot positioning based on error similarity, Acta Aeronaut. Astronaut. Sin., 2020, 41, (11), pp 423–434. https://doi.org/10.7527/S1000-6893.2020.24105 Google Scholar
Lei, P. and Zheng, L.Y. An automated in-situ alignment approach for finish machining assembly interfaces of large-scale components, Robot. Comput.-Integr. Manufact., 2017, 46, pp 130–143. https://doi.org/10.1016/j.rcim.2017.01.004 CrossRefGoogle Scholar
Zhu, Y.G., Den, B. and Huo, Z.S. Assembly gross error identification of small sample aircraft structure driven by inspection data and expert knowledge, Comput. Integr. Manufact. Syst., 2021, 27, (12), pp 3462–3474. https://doi.org/10.13196/j.cims.2021.12.008 Google Scholar
Liu, Y.J., Tian, X.T., Geng, J.H., et al. Modeling and simulation analysis of aircraft thin-walled parts assembly deviation, Mach. Tool Hydraul., 2020, 48, (02), pp 138–143. https://doi.org/10.3969/j.issn.1001-3881.2020.02.031 CrossRefGoogle Scholar
Ye, C.P., Liu, G.H., He, X.L., et al. Damage identification based on transfer entropy and improved surrogate data method, J. Central South Univ. (Sci. Technol.), 2019, 50, (12), pp 3023–3034. https://doi.org/10.11817/j.issn.1672-7207.2019.12.012 Google Scholar
Jin, X.Z., Ding, X.D. and Zhao, L.H. Variables selection for nonlinear system time series prediction model by transfer entropy, Proc. CSEE, 2018, 38, (S1), pp 192–200. https://doi.org/10.13334/j.0258-8013.pcsee.181085 Google Scholar
Liu, G.H., Yu, Z.Y., Liang, X., et al. Identification of nonlinearity using transfer entropy combined with surrogate data algorithm, J. Eng. Mech., 2019, 145, (2), p 04018138. https://doi.org/10.1061/(ASCE)EM.1943-7889.0001566 CrossRefGoogle Scholar
Wu, M.G., Wang, Z.K., Gan, X.S., et al. Identification of key flight conflict nodes based on complex network theory, J. Northwestern Polytech. Univ., 2020, 38, (02), pp 279–287. https://doi.org/10.1051/jnwpu/20203820279 CrossRefGoogle Scholar
Chen, L., Zheng, Y. and Li, SY. Evaluation approach for change propagation based on product feature network, Comput. Integr. Manuf. Syst., 2019, 25, (11), pp 2905–2912. https://doi.org/10.13196/j.cims.2019.11.019 Google Scholar
Zhang, H. and Lyu, Y.J. Vulnerability of vehicular ad hoc network based on complex network, J. Beijing Univ. Aeronaut. Astronaut., 2021, 47, (08), pp 1543–1549. https://doi.org/10.13700/j.bh.1001-5965.2020.0292 Google Scholar