Advertisement

Determining Granularity Level in Product Design Architecture

  • Tarek AlGeddawy
  • Hoda ElMaraghy
Part of the Lecture Notes in Production Engineering book series (LNPE)

Abstract

Product architecture represents components grouped into modules that can be assembled later to constitute a specific variant. Literature provides Methods of clustering components into weakly related modules with strong interconnections between components within modules. The number of modules and their hierarchical relationships shape product architecture and determine the balance between modular design and components integration. A novel hierarchical clustering approach, based on the biological Cladistics analysis, has been developed to cluster Design Structure Matrix (DSM) widely used to promote modularity. It evaluates different granularity levels of the resulting hierarchy and finds the best granularity level for maximum modularity. An automotive Body-in-White of 38 different components is used as a case study. Results showed the superiority of the recommended modularity pattern and synthesized product architecture over other clustering techniques.

Keywords

Product Design Architecture Modularity Granularity 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ulrich, K.T., Eppinger, S.D.: Product design and development, 5th edn. McGraw-Hill Irwin, Boston (2012)Google Scholar
  2. 2.
    ElMaraghy, H.: Changing and Evolving Products and Systems - Models and Enablers. In: ElMaraghy, H. (ed.) Changeable and Reconfigurable Manufacturing Systems, ch. 2, pp. 25–45. Springer, London (2009)CrossRefGoogle Scholar
  3. 3.
    Xuehong, D., Jianxin, J., Tseng, M.M.: Architecture of product family: fundamentals and methodology. Concurrent Engineering: Research and Applications 9(Copyright 2002, IEE), 309–325 (2001)Google Scholar
  4. 4.
    AlGeddawy, T., ElMaraghy, H.: Product Variety Management in Design and Manufac-turing: Challenges and Strategies. In: Proceeding of the 4th International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV 2011), Montreal, Canada (2011)Google Scholar
  5. 5.
    Eppinger, S.D., Browning, T.R.: Design structure matrix methods and applications. In: Engineering Systems 2012, xii, 334 p. MIT Press, Cambridge (2012)Google Scholar
  6. 6.
    Thebeau, R.E.: Knowledge Management of System Interfaces and Interactions for Product development Processes. In: System Design and Management 2001. Massachusetts Institute of Technology (2001)Google Scholar
  7. 7.
    Tian-Li, Y., Yassine, A.A., Goldberg, D.E.: An information theoretic method for de-veloping modular architectures using genetic algorithms. Research in Engineering Design 18(2), 91–109 (2007)CrossRefGoogle Scholar
  8. 8.
    Pandremenos, J., Chryssolouris, G.: A neural network approach for the development of modular product architectures. International Journal of Computer Integrated Manufacturing 24(10), 879–887 (2011)CrossRefGoogle Scholar
  9. 9.
    Sharman, D.M., Yassine, A.A.: Characterizing complex product architectures. Systems Engineering 7(1), 35–59 (2004)CrossRefGoogle Scholar
  10. 10.
    Chiriac, N., Holtta-Otto, K., Lysy, D., Eun Suk, S.: Level of Modularity and Different Levels of System Granularity. Journal of Mechanical Design 133(10), 101007 (2011)CrossRefGoogle Scholar
  11. 11.
    Hennig, W.: Phylogenitic Systematics. University of Illinois Press, Urbana (1966); republished in 1999Google Scholar
  12. 12.
    ElMaraghy, H., AlGeddawy, T., Azab, A.: Modelling evolution in manufacturing: A biological analogy. CIRP Annals - Manufacturing Technology 57(1), 467–472 (2008)CrossRefGoogle Scholar
  13. 13.
    Nixon, K.C.: The Parsimony Ratchet, a New Method for Rapid for Rapid Parsimony Analysis. Cladistics 15(1), 407–414 (1999)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Pandremenos, J., Chatzikomis, C., Chryssolouris, G.: On the Quantification of Interface Design Architectures. AIJSTPME 2(3), 41–48 (2009)Google Scholar
  15. 15.
    Millet, D., Yvars, P.-A., Tonnelier, P.: A method for identifying the worst recycling case: Application on a range of vehicles in the automotive sector. Resources, Conservation and Recycling 68(0), 1–13 (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Intelligent Manufacturing Systems CentreUniversity of WindsorOntarioCanada

Personalised recommendations