Advertisement

Fixture layout optimization in multi-station sheet metal assembly considering assembly sequence and datum scheme

  • Abolfazl Masoumi
  • Vahid Jandaghi Shahi
ORIGINAL ARTICLE
  • 102 Downloads

Abstract

One of the most influential causes of dimensional inaccuracy in automotive bodies is the inescapable fixture deviation, which is propagated in a Multi-Station Assembly (MSA) process. Therefore, a robust fixture design is necessary to increase product quality. The aim of this paper is to present a methodology for simultaneous optimization of fixture layout and assembly configuration, including assembly sequence and datum shift scheme, in MSA. To model the accumulated variation from one station to the downstream stations, a non-linear state-space representation is adopted. Feasible assembly sequences are automatically generated using a liaison graph and Breadth-First Search algorithm, and also all datum scheme options are provided based on a permutation method that is dependent on the number of parts and stations. Further, the sum of squared standard deviations for key product characteristics to be minimized is an optimization object. Part boundaries, design requirements, and assembly configuration feasibility are the constraints. This constrained problem is converted into an unconstrained one by two strategies: (i) Geometry boundaries and assembly configuration constraints are fulfilled through removing infeasible solutions from the design space and (ii) Design requirements are met by using a self-adaptive penalty function. Finally, the unconstrained problem is optimized using a genetic algorithm. The proposed methodology improved assembly process capability by 68 to 84% for an automotive body side in a case study.

Keywords

Multi-station assembly process Fixture layout optimization Assembly sequence Datum scheme Non-linear state space model 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

The authors wish to thank School of Mechanical Engineering at University of Tehran (Iran) and Digital Lifecycle Management group at University of Warwick (UK) for their invaluable cooperation during this research.

References

  1. 1.
    Shi J (2006) Stream of variation modeling and analysis for multistage manufacturing processes. CRC press Part II, Tylor and Francis group, Boca Raton, pp 117–140Google Scholar
  2. 2.
    Ceglarek D, Shi J (1995) Dimensional variation reduction for automotive body assembly. Manuf Rev 8 (2):139–154Google Scholar
  3. 3.
    Shiu BW, Ceglarek D, Shi J (1996) Multi-stations sheet metal assembly modeling and diagnostics. Trans NAMRI/SME 24:199–204Google Scholar
  4. 4.
    Mantripragada R, Whitney DE (1999) Modeling and controlling variation propagation in mechanical assemblies using state transition models. IEEE Trans Robot Automat 15(1):124–140CrossRefGoogle Scholar
  5. 5.
    Franciosa P, Gerbino S, Patalano S (2010) Variational modeling and assembly constraints in tolerance analysis of rigid part assemblies planar and cylindrical features. Int J Adv Manuf Technol 49(1):239–251CrossRefGoogle Scholar
  6. 6.
    Wang H, Rong Y, Li H, Shaun P (2010) Computer aided fixture design- recent research and trends. Comput Aided Des 42(12):1085–1094CrossRefGoogle Scholar
  7. 7.
    Abellán-Nebot JV, Subirón FR, Mira JS (2013) Manufacturing variation models in multi-station machining systems. Int J Adv Manuf Technol 64(1):63–83CrossRefGoogle Scholar
  8. 8.
    Xing Y, Chen G, Lai X, Jin S, Zhou J (2007) Assembly sequence planning of automobile body components based on liaison graph. Assembly Autom 27(2):157–164CrossRefGoogle Scholar
  9. 9.
    Ding Y, Ceglarek D, Shi J (2002) Design evaluation of multi-station assembly processes by using state space approach. J Mech Des 124(3):408–418CrossRefGoogle Scholar
  10. 10.
    Franciosa P, Gerbino S, Lanzotti A, Patalano S (2013) Automatic evaluation of variational parameters for tolerance analysis of rigid parts based on graphs. Int J Interact Des Manuf 7(4):239– 248CrossRefGoogle Scholar
  11. 11.
    Li Y, Chen G, Lai X, Jin S, Zheng C (2007) A framework of auto body assembly qualitative simulation system. Int J Adv Manuf Technol 33(11–12):244–1255Google Scholar
  12. 12.
    Hu SJ, Koren Y (1997) Stream-of-variation theory for automotive body assembly. CIRP Ann Manuf Technol 46(1):1–6CrossRefGoogle Scholar
  13. 13.
    Jin J, Shi J (1999) State space modeling of sheet metal assembly for dimensional control. J Manuf Sci Eng 121(4):756–762CrossRefGoogle Scholar
  14. 14.
    Ding Y, Shi J, Ceglarek D (2002) Diagnosability analysis of multi-station manufacturing processes. J Dyn Syst-T ASME 124(1):1–13CrossRefGoogle Scholar
  15. 15.
    Ding Y, Kim P, Ceglarek D, Jin J (2003) Optimal sensor distribution for variation diagnosis in multistation assembly processes. IEEE Trans Robot Automat 19(4):543–556CrossRefGoogle Scholar
  16. 16.
    Ding Y, Jin J, Ceglarek D, Shi J (2005) Process-oriented tolerancing for multi-station assembly systems. IIE Trans 37(6):493– 508CrossRefGoogle Scholar
  17. 17.
    Kim P, Ding Y (2004) Optimal design of fixture layout in multistation assembly processes. IEEE T Autom Sci Eng 1(2):133–145CrossRefGoogle Scholar
  18. 18.
    Li Z, Izquierdo LE, Kokkolaras M, Hu SJ, Papalambros PY (2008) Multiobjective optimization for integrated tolerance allocation and fixture layout design in multistation assembly. J Manuf Sci Eng 130(4):044501CrossRefGoogle Scholar
  19. 19.
    Phoomboplab T, Ceglarek D (2008) Process yield improvement through optimum design of fixture layouts in 3D multistation assembly systems. J Manuf Sci Eng 130(6):061005CrossRefGoogle Scholar
  20. 20.
    Izquierdo LE, Hu SJ, Du H, Jin R, Jee H, Shi J (2009) Robust fixture layout design for a product family assembled in a multistage reconfigurable line. J Manuf Sci Eng 131(4):041008CrossRefGoogle Scholar
  21. 21.
    Xie W, Deng Z, Ding B, Kuang H (2015) Fixture layout optimization in multi-station assembly processes using augmented ant colony algorithm. J Manuf Syst 37(1):277–289CrossRefGoogle Scholar
  22. 22.
    Tyagi S, Shukla N, Kulkarni S (2015) Optimal design of fixture layout in a multi-station assembly using highly optimized tolerance inspired heuristic. Appl Math Model 40(11-12):6134–6147MathSciNetCrossRefGoogle Scholar
  23. 23.
    Cai W (2006) Robust pin layout design for sheet-panel locating. Int J Adv Manuf Technol 28(5–6):486–494CrossRefGoogle Scholar
  24. 24.
    Bourjault A (1984) Contribution a une approche methodologique de assemblage automatise: elaboration automatique sequences operatories. PhD Thesis, Universite de Franche Comte, BesanconGoogle Scholar
  25. 25.
    Rashid MFF, Hutabarat W, Tiwari A (2011) A review on assembly sequence planning and assembly line balancing optimization using soft computing approaches. Int J Adv Manuf Technol 59(1–4):335–349Google Scholar
  26. 26.
    Whitney D (2004) Mechanical assemblies: their design, manufacture, and role in product development. Oxford series on advanced manufacturing. Oxford S(1):201–231Google Scholar
  27. 27.
    Wang H, Ceglarek D (2005) Quality-driven sequence planning for compliant structure assemblies. CIRP Ann Manuf Techno 54(1):31–35CrossRefGoogle Scholar
  28. 28.
    Lai XM, Xing YF, Sun J, Chen GL (2009) Optimisation of assembly sequences for compliant body assemblies. Int J Prod Res 47(21):6129–6143CrossRefGoogle Scholar
  29. 29.
    Xing Y, Wang Y (2012) Assembly sequence planning based on a hybrid particle swarm optimisation and genetic algorithm. Int J Prod Res 50(24):7303–7312CrossRefGoogle Scholar
  30. 30.
    Tian Z, Lai X, Lin Z (2009) Robust fixture layout design for multi-station sheet metal assembly processes using a genetic algorithm. Int J Prod Res 47(21):6159–6176CrossRefGoogle Scholar
  31. 31.
    Qu X, Li X, Ma Q, Wang X (2016) Variation propagation modeling for locating datum system design in multi-station assembly processes. Int J Adv Manuf Technol 86(5):1357–1366CrossRefGoogle Scholar
  32. 32.
    Tessema B, Yen G (2006) A self-adaptive penalty function based algorithm for constrained optimization. IEEE Congress on Evolutionary Computation, pp 246–253Google Scholar
  33. 33.
    Back T (1994) Selective pressure in evolutionary algorithms: a characterization of selection mechanisms. In: Proceedings of the first IEEE conference on evolutionary computation. IEEE World Congress on Computational IntelligenceGoogle Scholar
  34. 34.
    Michalewicz Z (1996) Chapter 16 Genetic algorithms + data structures = evolution programs. Springer, New YorkGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mechanical Engineering, College of EngineeringUniversity of TehranTehranIran

Personalised recommendations