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Assisted Decision-Making for Assembly Technique Selection and Geometrical Tolerance Allocation

  • Loïc AndolfattoEmail author
  • François Thiébaut
  • Claire Lartigue
  • Marc Douilly
Part of the Lecture Notes in Production Engineering book series (LNPE)

Abstract

Assembly process planning involves many aspects from geometrical matters to operational research. Though, the literature shows very few works about assembly technique selection.

This paper deals with an original method to select assembly techniques and to allocate component geometrical tolerances in order to minimize the product cost and to maximize the conformity rate associated with the assembly plan.

The data structures used to define a parametric assembly plan is detailed. This data structure is used to formulate a multi-objective optimization problem reflecting the concerns of the study.

The entire method is illustrated trough a case study. The results obtained are presented and followed by a discussion about the potential benefits of its application in an industrial context. The useful support that this method can provide to decision-making is highlighted. Its shared point of view from product designers to manufacturing process designers makes it an efficient tool for concurrent engineering.

Keywords

Assembly process planning assembly technique selection geometrical tolerance allocation multi-objective optimization concurrent engineering 

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References

  1. 1.
    Boothroyd, G., Dewhurst, P.: Product design for manufacture and assembly. Manufacturing Engineering 100, 42–46 (1988)Google Scholar
  2. 2.
    Wang, L., Keshavarzmanesh, S., Feng, H.Y., Buchal, R.: Assembly process planning and its future in collaborative manufacturing: a review. The International Journal of Advanced Manufacturing Technology 41, 132–144 (2009)CrossRefGoogle Scholar
  3. 3.
    Bourjault, A.: Contribution à une Approche Méthodologique de L’Assemblage Automatisé: Élaboration Automatique des Sequences Opératoires. Ph.D. thesis, Universite de Franche-Comte (1984)Google Scholar
  4. 4.
    De Fazio, T., Whitney, D.: Simplified generation of all mechanical assembly sequences. IEEE Journal of Robotics and Automation 3(6), 640–658 (1987)CrossRefGoogle Scholar
  5. 5.
    Dini, G., Failli, F., Lazzerini, B., Marcelloni, F.: Generation of optimized assembly sequences using genetic algorithms. CIRP Annals - Manufacturing Technology 48(1), 17–20 (1999)CrossRefGoogle Scholar
  6. 6.
    Homem de Mello, L., Sanderson, A.: A correct and complete algorithm for the generation of mechanical assembly sequences. IEEE Transactions on Robotics and Automation 7(2), 228–240 (1991)CrossRefGoogle Scholar
  7. 7.
    Mantripragada, R., Whitney, D.: The datum flow chain: A systematic approach to assembly design and modeling. Research in Engineering Design 10(3), 150–165 (1998)CrossRefGoogle Scholar
  8. 8.
    Cao, T., Sanderson, A.: Task decomposition and analysis of robotic assembly task plans using petri nets. IEEE Transactions on Industrial Electronics 41(6), 620–630 (1994)CrossRefGoogle Scholar
  9. 9.
    Becker, C., Scholl, A.: A survey on problems and methods in generalized assembly line balancing. European Journal of Operational Research 168(3), 694–715 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    CIRP: Dictionnary of Production Engineering; vol. IV - Assembly (2012)Google Scholar
  11. 11.
    Abdullah, T.A., Popplewell, K., Page, C.J.: A review of the support tools for the process of assembly method selection and assembly planning. International Journal of Production Research 41(11), 2391–2410 (2003)CrossRefGoogle Scholar
  12. 12.
    Camelio, J., Hu, S.J., Ceglarek, D.: Modeling variation propagation of multi-station assembly systems with compliant parts. Journal of Mechanical Design 125(4), 673–681 (2003)CrossRefGoogle Scholar
  13. 13.
    Mounaud, M., Thiébaut, F., Bourdet, P., Falgarone, H., Chevassus, N.: Assembly sequence influence on geometric deviations propagation of compliant parts. International Journal of Production Research 49(4), 1021–1043 (2011)CrossRefGoogle Scholar
  14. 14.
    Marguet, B., Chevassus, N., Falgarone, H., Bourdet, P.: Geometrical behavior laws for computer aided tolerancing: Anatole a tool for structural assembly tolerance analysis. In: 8th CIRP Seminar on Computer-Aided Tolerancing, Charlotte, NC USA (2003)Google Scholar
  15. 15.
    Chase, K., Greenwood, W., Loosli, B., Hauglund, L.: Least cost tolerance allocation for mechanical assemblies with automated process selection. Manufacturing Review 3(1), 49–59 (1990)Google Scholar
  16. 16.
    Inspyred 1.0: Bio-inspired Algorithms in Python (May 2012), http://inspyred.github.com/ (consulted on May 4, 2012)
  17. 17.

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Loïc Andolfatto
    • 1
    • 2
    Email author
  • François Thiébaut
    • 2
    • 3
  • Claire Lartigue
    • 2
    • 3
  • Marc Douilly
    • 1
  1. 1.EADS Innovation WorksSuresnesFrance
  2. 2.LURPAENS de CachanCachan CedexFrance
  3. 3.IUT de CachanCachan CedexFrance

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