Hostname: page-component-7f64f4797f-p4db6 Total loading time: 0 Render date: 2025-11-04T13:12:08.550Z Has data issue: false hasContentIssue false
  • English
  • Français

A configuration design based method for platform commonization for product families

Published online by Cambridge University Press:  28 January 2005

BRIAN CORBETT  and
DAVID W. ROSEN
BRIAN CORBETT
Affiliation:
Bell Helicopter Textron Inc., Fort Worth, Texas 76101, USA
DAVID W. ROSEN
Affiliation:
The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0405, USA
Get access Rights & Permissions [Opens in a new window]

Abstract

Product families help companies reach customers in several different markets, lessen the time needed to develop new products, and reduce costs by sharing common components among many products. The product platform can be considered as a set of technologies, components, or functions, and their arrangements, that are utilized for more than one product. Configuration design focuses on the components in a product and their connections and relationships. Discrete, combinatorial design spaces are used to model design requirements regarding physical connections, module partitions, and assembly sequences for the product family. To ensure that products satisfy all design requirements, it is necessary to combine these design spaces into a common configuration space into which all requirements can be mapped. This paper presents computational methods for modeling and combining design spaces so those configurations can be identified that satisfy all constraints. A new representation of assembly sequences facilitates the development of an assembly design space, elements of which can be enumerated readily. Because the size of the combinatorial design spaces can become quite large, computational efficiency is an important consideration. A new designer guided method, called the partitioning method, is presented for decomposing configuration design problems in a hierarchical manner that enables significant reductions in design space sizes. An example of a family of automotive underbodies illustrates the application of the discrete design space approach to develop a common platform.

Keywords

Configuration DesignDiscrete MathematicsPlatform CommonizationProduct Family Design

Information

Type
Research Article
Information
AI EDAM , Volume 18 , Issue 1 , February 2004 , pp. 21 - 39
Copyright
© 2004 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Agarwal, M. & Cagan, J. (1998). A blend of different tastes: The language of coffeemakers. Environment and Planning B: Planning and Design 25(2), 205–226.Google Scholar
Agarwal, M., Cagan, J., & Constantine, K.G. (1999). Influencing generative design through continuous evaluation: Associating cost with the coffeemaker shape grammar. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 13(4), 253–275.Google Scholar
Corbett, B. (2003). Configuration design methods and mathematics for product families. MS thesis. Georgia Institute of Technology.
Dixon, J.R., Duffey, M.R., Irani, R.K., Meunier, K.L., & Orelup, M.F. (1988). A proposed taxonomy of mechanical design problems. Proc. ASME Computers in Engineering Conf., San Francisco, pp. 41–46.
Finger, S. & Dixon, J.R. (1989). A review of research in mechanical engineering design. Part I: Descriptive, prescriptive, and computer based models of design processes. Research in Engineering Design 1, 51–67.Google Scholar
Gero, J.S., Kazakov, V., & Schnier, T. (1997). Genetic engineering and design problems. In Evolutionary Algorithms in Engineering Applications (Dasgupta, D. & Michalewicz, Z., Eds.), pp. 47–68. Berlin: Springer–Verlag.
Gonzalez–Zugasti, J. P., Otto, K.N., & Baker, J. D. (1998). A method for architecting product platforms with an application to interplanetary mission design. Proc. ASME Design Automation Conf., Atlanta, GA, Paper No. DETC98/DAC-5608.
Gonzalez–Zugasti, J.P., Otto, K.N., & Baker, J.D. (1999). Assessing value for product family and selection. Proc. ASME Design Automation Conf., Las Vegas, NV, Paper No. DETC99/DAC-8613.
Heinrich, M. & Jungst, E. (1991). A resource-based paradigm for the configuring of technical systems from modular components. Proc. Seventh IEEE Conf. on AI Applications, pp. 257–264.
Kolodner, J. (1993). Case-Based Reasoning. San Mateo, CA: Morgan Kaufmann.
Kota, S. & Sethuraman, K. (1998). Managing variety in product families through design for commonality. Proc. ASME Design Theory and Methodology Conf., Atlanta, GA. Paper No. DETC98/DTM-5651.
Kota, S. & Ward, A.C. (1990). Functions, structures, and constraints in conceptual design. Proc. ASME Design Theory and Methodology Conf., Chicago, DE-Vol. 27, pp. 239–250.
Kreher, D.L. & Stinson, D.R. (1999). Combinatorial Algorithms-Generation, Enumeration, and Search. Boca Raton, FL: CRC Press.
Lomnicki, Z.A. (1972). Two-terminal series-parallel networks. Advances in Applied Probability 4, 109–150.Google Scholar
MacMahon, P.A. (1891). Yoke-chains and multipartite compositions in connexion with the analytical forms called “trees.” Proceedings of the London Mathematical Society 22, 330–346.Google Scholar
Martin, M.V. & Ishii, K. (1997). Design for variety: development of complexity indices and design charts. Proc. ASME Design for Manufacturing Conf., Paper No. DETC97/DFM-4359. Sacramento, CA.
Mittal, S. & Falkenheiner, B. (1990). Dynamic constraint satisfaction problems. Proc. AAAI-90 Eighth National Conf. on AI, pp. 25–32.
Munkres, J.R. (1975). Topology, a First Course. Englewood Cliffs, NJ: Prentice Hall.
Newcomb, P.J., Bras, B.A., & Rosen, D.W. (1998). Implications of modularity on product design for the life cycle. ASME Journal of Mechanical Design 120(3), 483–490.Google Scholar
Nugen, F. (1999). Enumerating with constraints on the hierarchy space. Unpublished manuscript, Georgia Institute of Technology.
Reddy, G. & Cagan, J. (1995). An improved shape annealing algorithm for truss topology generation. ASME Journal of Mechanical Design 117, 315–321.Google Scholar
Rosen, D.W. & Dixon, J.R. (1992). Languages for feature-based design and manufacturability evaluation. International Journal of Systems Automation: Research and Applications 2(4), 353–373.Google Scholar
Rosenman, M.A. & Gero, J.S. (1999). Evolving designs by generating useful complex gene structures. In Evolutionary Design by Computers (Bently, P.J., Ed.). San Francisco, CA: Morgan Kaufmann.
Sacerdoti, E.D. (1974). Planning in a hierarchy of abstraction spaces. Artificial Intelligence 5, 115–135.Google Scholar
Siddique, Z. & Rosen, D.W. (1999). Product platform design: A graph grammar approach. Proc. ASME Design Theory and Methodology Conf., Las Vegas, NV, Paper No. DETC99/DTM-8762.
Siddique, Z. & Rosen, D.W. (2001). On combinatorial design spaces for the configuration design of product families. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15(2), 91–108.Google Scholar
Soininen, T., Tiihonen, J., Mannisto, T., & Sulonen, R. (1998). Towards a general ontology of configuration. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 12(4), 357–372.Google Scholar
Steward, D.V. (1981). Systems Analysis and Management: Structure, Strategy, and Design. New York: Petrocelli Books.
Stone, R.B., Wood, K.L., & Crawford, R.H. (1998). A heuristic method to identify modules from a functional description of a product. Proc. ASME Design Theory and Methodology Conf., Atlanta, GA, Paper No. DETC98/DTM-5642.
Ulrich, K.T. & Eppinger, S.D. (2000). Product Design and Development, 2nd ed. Boston: McGraw–Hill.