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    Wetmore, William R. Summers, Joshua David and Greenstein, Joel S. 2010. Experimental study of influence of group familiarity and information sharing on design review effectiveness. Journal of Engineering Design, Vol. 21, Issue. 1, p. 111.


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  • Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Volume 16, Issue 4
  • September 2002, pp. 291-302

FuzzySTAR: Fuzzy set theory of axiomatic design review

  • GEORGE Q. HUANG (a1) and ZUHUA JIANG (a2)
  • DOI: http://dx.doi.org/10.1017/S0890060402164031
  • Published online: 16 December 2002
Abstract

Product development involves multiple phases. Design review (DR) is an essential activity formally conducted to ensure a smooth transition from one phase to another. Such a formal DR is usually a multicriteria decision problem, involving multiple disciplines. This paper proposes a systematic framework for DR using fuzzy set theory. This fuzzy approach to DR is considered particularly relevant for several reasons. First, information available at early design phases is often incomplete and imprecise. Second, the relationships between the product design parameters and the review criteria cannot usually be exactly expressed by mathematical functions due to the enormous complexity. Third, DR is frequently carried out using subjective expert judgments with some degree of uncertainty. The DR is defined as the reverse mapping between the design parameter domain and design requirement (review criterion) domain, as compared with Suh's theory of axiomatic design. Fuzzy sets are extensively introduced in the definitions of the domains and the mapping process to deal with imprecision, uncertainty, and incompleteness. A simple case study is used to demonstrate the resulting fuzzy set theory of axiomatic DR.

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Corresponding author
Reprint requests to: Department of Industrial and Manufacturing Systems Engineering, University of Hong Kong, Pokfulam Road, Hong Kong, PRC. E-mail: gqhuang@hkucc.hku.hk
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AI EDAM
  • ISSN: 0890-0604
  • EISSN: 1469-1760
  • URL: /core/journals/ai-edam
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