Skip to main content

Advertisement

SpringerLink
Log in

Multi-objective optimisation of assembly fixturing layout for large composite fuselage panel reinforced by frames and stringers

  • Original Article
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

To improve the rigidity of large composite fuselage panels and reduce the deformation caused by factors such as gravity during the assembly locating process, this paper presents a multi-objective optimisation method of the discrete fixturing layout based on the integration of a back-propagation neural network (BPNN) with the elitist nondominated sorting genetic algorithm (NSGA-II), which considers the characteristics of the composite fuselage panel, such as its complex structure and high tendency to undergo damage when subjected to force, in addition to the control the fixturing system cost. This method can simultaneously determine the optimal number and positions of tooling fixturing points. First, a binary integer variable is proposed to represent the fixturing layout, to realise the coupling representation of the number and positions of the fixturing points. To improve the optimisation efficiency, after comprehensively comparing the prediction accuracies of several commonly used surrogate models, a BPNN prediction model is constructed to describe the nonlinear mapping relationship between the fixturing layout and the maximum deformation (maximum Mises stress) of the composite fuselage panel based on the limited samples obtained by finite element analysis (FEA) and minimax Latin hypercube sampling (MLHS). Thereafter, the allowable deformation of the composite fuselage panel is considered the constraint; the number of fixturing points and the stress of the panel are used as the optimisation objectives. The Pareto solution set of the fixturing layout is determined using NSGA-II. Finally, a composite front fuselage panel case study of a wide-body airliner is considered to demonstrate the effectiveness of the proposed method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data availability

The data is available if required by the reviewers/editors.

Code availability

The code is available if required by the reviewers/editors.

References

  1. Niu CY, Cheng XQ, Zhang JK (2010) Composite airframe structures. Aviation industry press, Beijing

    Google Scholar 

  2. Cai W, Hu SJ, Yuan JX (1996) Deformable sheet metal fixturing: principles, algorithms, and simulations. J Manuf Sci Eng-Trans Asme 118(3):318–324. https://doi.org/10.1115/1.2831031

    Article  Google Scholar 

  3. Ceglarek D, Li HF, Tang Y (2001) Modeling and optimization of end effector layout for handling compliant sheet metal parts. J Manuf Sci Eng 123(3):473–480. https://doi.org/10.1115/1.1366682

    Article  Google Scholar 

  4. Krishnakumar K, Melkote SN (2000) Machining fixture layout optimization using the genetic algorithm. Int J Mach Tools Manuf 40(4):579–598. https://doi.org/10.1016/s0890-6955(99)00072-3

    Article  Google Scholar 

  5. Liao YG (2003) A genetic algorithm-based fixture locating positions and clamping schemes optimization. Proc IME Part B-J Eng Manuf 217(8):1075–1083. https://doi.org/10.1177/095440540321700805

    Article  Google Scholar 

  6. Padmanaban KP, Arulshri KP, Prabhakaran G (2009) Machining fixture layout design using ant colony algorithm based continuous optimization method. Int J Adv Manuf Technol 45(9–10):922–934. https://doi.org/10.1007/s00170-009-2035-6

    Article  Google Scholar 

  7. Dou JP, Wang XS, Wang L (2012) Machining fixture layout optimization under dynamic conditions based on evolutionary techniques. Int J Prod Res 50(15):4294–4315. https://doi.org/10.1080/00207543.2011.618470

    Article  Google Scholar 

  8. Cheng H, Li Y, Zhang KF, Luan C, Xu YW, Li MH (2012) Optimization method of fixture layout for aeronautical thin-walled structures with automated riveting. Assem Autom 32:323–332. https://doi.org/10.1108/01445151211262384

    Article  Google Scholar 

  9. Hu FW (2012) Research on aircraft skins trimming process in a reconfigurable fixture: modelling, simulation, optimization and system development. Dissertation, Beihang University

  10. Xing YF, Hu M, Zeng H, Wang YS (2015) Fixture layout optimization based on a non-domination sorting social radiation algorithm for auto-body parts. Int J Prod Res 53(11):3475–3490. https://doi.org/10.1080/00207543.2014.1003662

    Article  Google Scholar 

  11. Khodabandeh M, Saryazdi MG, Ohadi A (2020) Multi-objective optimization of auto-body fixture layout based on an ant colony algorithm. Proc IMechE Part C: J Mech Eng Sci 234(6):1137–1145. https://doi.org/10.1177/0954406219891756

    Article  Google Scholar 

  12. Simpson TW, Booker AJ, Ghosh D, Giunta AA, Koch PN, Yang RJ (2004) Approximation methods in multidisciplinary analysis and optimization: a panel discussion. Struct Multidiscip Optim 27(5):302–313. https://doi.org/10.1007/s00158-004-0389-9

    Article  Google Scholar 

  13. Vasundara M, Padmanaban KP, Sabareeswaran M, RajGanesh M (2012) Machining fixture layout design for milling operation using FEA, ANN and RSM. In: Rajesh R, Ganesh K, Koh SCL (eds) International conference on modelling optimization and computing, vol 38. Procedia engineering. Elsevier Science Bv, Amsterdam, 1693–1703. https://doi.org/10.1016/j.proeng.2012.06.206

  14. Lu C, Zhao HW (2015) Fixture layout optimization for deformable sheet metal workpiece. Int J Adv Manuf Technol 78(1–4):85–98. https://doi.org/10.1007/s00170-014-6647-0

    Article  Google Scholar 

  15. Qin G, Wang Z, Rong Y, Li Q (2015) A unified approach to multi-fixturing layout planning for thin-walled workpiece. Proc the IME Part B: J Eng Manuf 231(3):454–469. https://doi.org/10.1177/0954405415585240

    Article  Google Scholar 

  16. Wang Z, Yang B, Kang Y, Yang Y (2016) Development of a prediction model based on RBF neural network for sheet metal fixture locating layout design and optimization. Comput Intell Neurosci 2016:1–6. https://doi.org/10.1155/2016/7620438

    Article  Google Scholar 

  17. Wang Z, Yang Y, Yang B, Kang Y (2016) Optimal sheet metal fixture locating layout by combining radial basis function neural network and bat algorithm. Adv Mech Eng 8(12):1–10. https://doi.org/10.1177/1687814016681905

    Article  Google Scholar 

  18. Yang B, Wang Z, Yang Y, Kang Y, Li X (2017) Optimum fixture locating layout for sheet metal part by integrating kriging with cuckoo search algorithm. Int J Adv Manuf Technol 91(1–4):327–340. https://doi.org/10.1007/s00170-016-9638-5

    Article  Google Scholar 

  19. Yang Y, Wang Z, Yang B, Jing Z, Kang Y (2017) Multiobjective optimization for fixture locating layout of sheet metal part using SVR and NSGA-II. Math Problems Eng 8:1–10. https://doi.org/10.1155/2017/7076143

    Article  Google Scholar 

  20. Yue X, Wen Y, Hunt JH, Shi J (2018) Surrogate model-based control considering uncertainties for composite fuselage assembly. J Manuf Sci Eng 140(4):041017. https://doi.org/10.1115/1.4038510

    Article  Google Scholar 

  21. Sacks J, Welch WJ, Mitchell TJ, Wynn HP (1989) Design and analysis of computer experiments. Stat Sci 4:409–423. https://doi.org/10.1214/ss/1177012413

    Article  MathSciNet  MATH  Google Scholar 

  22. Santner TJ, Williams BJ, Notz WI (2003) The design and analysis of computer experiments. Springer, New York

    Book  MATH  Google Scholar 

  23. Vapnik VN (1995) The nature of statistical learning theory. Springer, New York

    Book  MATH  Google Scholar 

  24. Haykin SS (2009) Neural networks and learning machines. Pearson Education, NJ

    Google Scholar 

  25. Konak A, Coit DW, Smith AE (2006) Multi-objective optimization using genetic algorithms: a tutorial. Reliab Eng Syst Saf 91(9):992–1007. https://doi.org/10.1016/j.ress.2005.11.018

    Article  Google Scholar 

  26. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New York

    MATH  Google Scholar 

  27. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197. https://doi.org/10.1109/4235.996017

    Article  Google Scholar 

Download references

Funding

This work was supported by National Natural Science Foundation of China (Grant number 52105502), the Fundamental Research Funds for the Central Universities (Grant number 3042021601), and Fund of National Engineering and Research Center for Commercial Aircraft Manufacturing (Grant numbers COMAC-SFGS-2019–3731 and COMAC-SFGS-2019–263).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China

    Zhanghao Wang, Dongsheng Li, Xuce Dong & Yunong Zhai

  2. Commercial Aircraft Corporation of China, Ltd, Shanghai, 201324, China

    Liheng Shen

Authors
  1. Zhanghao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dongsheng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liheng Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xuce Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yunong Zhai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yunong Zhai.

Ethics declarations

Ethics approval

The authors complied with all the rules regarding the ethics in publication.

Consent to participate

All the authors agree to the data provided in the text and the order of authorship.

Consent for publication

All the authors agree to publication immediately after acceptance.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Z., Li, D., Shen, L. et al. Multi-objective optimisation of assembly fixturing layout for large composite fuselage panel reinforced by frames and stringers. Int J Adv Manuf Technol 125, 1403–1418 (2023). https://doi.org/10.1007/s00170-022-10776-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-022-10776-1

Keywords

Search

Navigation