Hostname: page-component-8448b6f56d-gtxcr Total loading time: 0 Render date: 2024-04-23T18:19:46.085Z Has data issue: false hasContentIssue false

Development of design methodology for upgradable products based on function–behavior–state modeling

Published online by Cambridge University Press:  07 October 2005

YASUSHI UMEDA
Affiliation:
Department of Mechanical Engineering, Graduate School of Engineering, Osaka University, Yamada-Oka 2-1, Suita, Osaka 565-0871, Japan
SINSUKE KONDOH
Affiliation:
National Institute of Science and Technology, Tsukuba, Japan
YOSHIKI SHIMOMURA
Affiliation:
Faculty of System Design, Tokyo Metropolitan University, Tokyo 192-0397, Japan
TETSUO TOMIYAMA
Affiliation:
Faculty of Maritime and Materials Engineering, Delft University of Technology, Delft, The Netherlands

Abstract

Extending product life is one of the hopeful approaches to reduce the environmental issue, which is one of the most critical issues of today. However, many products are thrown away because of obsolescence of functions and their performance. Therefore, we should design products to be functionally upgradable. Moreover, such upgradable products may create business chances at later stages of product life cycles. The objective of this research is to propose a design methodology for upgradability. This methodology employs a functional modeling scheme, FBS modeling, because upgrade design is a distinctive application of functional design that aims at maximizing functional flexibility with minimal structural changes after the product is manufactured. Here, the functional flexibility refers to an ability of a product to adapt its functions to changes of user needs. This paper proposes and models design processes and design operations in the upgrade design. Especially, the methodology supports finding out candidates of modifications of the function structure and configuration of a platform, which is common structure of a product among several generations, and upgrade modules. One of its central issues of upgrade design is treatment of future uncertainty. For this purpose, we propose two design strategies: delayed selection of components, and expanding and shrinking platform. A prototype system and a case study of upgrade design for a vacuum cleaner are also illustrated. The case study indicates that the system succeeded in systematically supporting a designer to execute the design methodology. Regarding the functional design, as an extension of FBS modeling, this paper proposes a method to relate abstract entity concepts in FBS modeling to concrete components through a quantitative behavior model and range calculation, in addition to deployment of FBS modeling for the design methodology.

Type
Research Article
Copyright
© 2005 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.)

References

REFERENCES

Bracewell, R.H. & Sharpe, J.E.E. (1996). Functional descriptions used in computer support for qualitative scheme generation—Schemebuilder. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10(4), 333346.Google Scholar
Chakrabarti, A. & Bligh, T.P. (2001). A scheme for functional reasoning in mechanical conceptual design. Design Studies 22(6), 493517.Google Scholar
Daimon, T., Kondoh, S., & Umeda, Y. (2004). Proposal of decision support method for life cycle strategy by estimating value and physical lifetimes. Proc. 11th Int. CIRP Life Cycle Engineering Seminar, pp. 4956.
Finch, W.W. & Ward, A.C. (1995). Generalized set-propagation operations over relations of more than three variables. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 9(3), 231242.Google Scholar
Forbus, K. (1984). Qualitative process theory. Artificial Intelligence 24(3), 85168.Google Scholar
Fujimoto, J., Umeda, Y., Tamura, T., Tomiyama, T., & Kimura, F. (2003). Development of service-oriented products based on the inverse manufacturing concept. Environmental Science & Technology 37(23), 53985406.Google Scholar
Fujita, K., Akagi, S., Yoneda, T., & Ishikawa, M. (1998). Simultaneous optimization of product family sharing system structure and configuration. Proc. ASME 1998 Design Engineering Technical Conf., Paper No. DETC98/DFM-5722.
Fujita, K., Sakaguchi, H., & Akagi, S. (1999). Product variety development and its optimization under modular architecture and module commonization. Proc. ASME 1999 Design Engineering Technical Conf., Paper No. DETC99/DFM-8923.
Fujita, K. (2002). Product variety optimization under modular architecture. Computer-Aided Design 34(12), 953965.Google Scholar
Gero, J.S. & Kannengiesser, U. (2004). The situated function–behaviour–structure framework. Design Studies 25(4), 373391.Google Scholar
Hiroshige, Y., Nishi, T., & Ohashi, T. (2001). Recyclability evaluation method (REM) and its application. Proc. EcoDesign 2001, pp. 315320. Los Alamitos, CA: IEEE Computer Society.
Ishii, K. (1999). Incorporating end-of-life strategy in product definition. Proc. EcoDesign'99, pp. 364369. Los Alamitos, CA: IEEE Computer Society.
Johnson, M.E. & Anderson, E. (2000). Postponement strategies for channel derivatives. International Journal of Logistics Management 11(1), 1935.Google Scholar
Jovane, F. & Alting, L. (1993). A key issue in product life cycle: disassembly. Annals of CIRP'93 42(2), 651658.Google Scholar
Kobayashi, H. (2001). Life cycle planning for strategic evolution of eco-products. Proc. 12th Int. Conf. Engineering Design (ICED01), pp. 757763.
Li, H. & Azarm, S. (2002). An approach for product line design selection under uncertainty and competition. Journal of Mechanical Design 124, 385392.Google Scholar
Martin, M.V. & Ishii, K. (2002). Design for variety: Developing standardized and modularized product platform architectures. Research in Engineering Design 13(4), 213235.Google Scholar
Matsuda, A., Shimomura, Y., Kondoh, S., & Umeda, Y. (2003). Upgrade planning for upgradable product design. Proc. EcoDesign 2003, pp. 231234. Los Alamitos, CA: IEEE Computer Society.
Messac, A., Martinez, M. P., & Simpson, T. W. (2002). Introduction of a product family penalty function using physical programming. Journal of Mechanical Design 124, 164172.Google Scholar
Nakamura, S. & Kondoh, Y. (2001). Waste input–output analysis of disposal, recycling, and extended life of electric home appliances. Proc. EcoDesign 2001, pp. 814819. Los Alamitos, CA: IEEE Computer Society.
Prabhakar, S. & Goel, A.K. (1998). Functional modeling for enabling adaptive design of devices for new environments. Artificial Intelligence in Engineering 12(4), 417444.Google Scholar
Qian, L. & Gero, J.S. (1996). Function–behavior–structure paths and their role in analogy-based design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10(4), 289312.Google Scholar
Sembugamoorthy, V. & Chandrasekaran, B. (1986). Functional representation of devices and compilation of diagnostic problem-solving systems. In Experience, Memory, and Reasoning (Riesbeck, C.K., Ed.). Hillsdale, NJ: Erlbaum.
Simpson, T.W., Maier, J.R.A., & Mistree, F. (2001). Product platform design: method and application. Research in Engineering Design 13(1), 222.Google Scholar
Suh, N.P. (1990). The Principles of Design. New York: Oxford University Press.
Suwa, M., Gero, J.S., & Purcell, T. (1999). Unexpected discoveries and s-inventions of design requirements: a key to creative designs. In Computational Models of Creative Design IV (Gero, J.S. & Maher, M.L., Eds.), pp. 297320. Sydney, Australia: Key Centre of Design Computing and Cognition, University of Sydney.
Tomiyama, T. (1997). A manufacturing paradigm toward the 21st century. Integrated Computer Aided Engineering 4, 159178.Google Scholar
Tomiyama, T., Kiriyama, T., & Yoshikawa, H. (1992). Conceptual design of mechanisms: A qualitative physics approach. In Concurrent Engineering: Automation, Tools, and Techniques (Kusiak, A., Ed.), pp. 131152, New York: Wiley.
Ulrich, K.T. (1995). The role of product architecture in the manufacturing firm. Research Policy 24, 419440.Google Scholar
Umeda, Y., Hijihara, K., Oono, M., Ogawa, Y., Kobayashi, H., Hattori, M., Masui, K., & Fukano, A. (2003). Proposal of life cycle design support method using disposal cause analysis matrix. Proc. 14th Int. Conf. Engineering Design (ICED03).
Umeda, Y., Ishii, M., Yoshioka, M., & Tomiyama, T. (1996). Supporting conceptual design based on the function–behavior–state modeler. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10(4), 275288.Google Scholar
Umeda, Y., Nonomura, A., & Tomiyama, T. (2000). Study on life-cycle design for the post mass production paradigm. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 14(2), 149161.Google Scholar
Umeda, Y., Shimomura, Y., Yoshioka, M., & Tomiyama, T. (1999). A proposal of design methodology for upgradable products. Proc. ASME 1999 Design Engineering Technical Conf., Paper No. DETC99/DFM-8969.
Umeda, Y., Takeda, H., Tomiyama, T., & Yoshikawa, H. (1990). Function, behaviour, and structure. In Applications of Artificial Intelligence in Engineering V (Gero, J.S., Ed.), pp. 177193. Southampton/Berlin: Computational Mechanics Publications/Springer–Verlag.
Umeda, Y. & Tomiyama, T. (1997). Functional reasoning in design. IEEE Expert: Intelligent Systems and Their Applications 12(2), 4248.Google Scholar
Umemori, Y., Kondoh, S., Umeda, Y., Shimomura, Y., & Yoshioka, M. (2001). Design for upgradable products considering future uncertainty. Proc. EcoDesign 2001, pp. 8792. Los Alamitos, CA: IEEE Computer Society.
Yoshioka, M., Umeda, Y., Takeda, H., Shimomura, Y., Nomaguchi, Y., & Tomiyama, T. (2004). Physical concept ontology for the knowledge intensive engineering framework. Advanced Engineering Informatics 18, 95113.Google Scholar