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Supporting conceptual design based on the function-behavior-state modeler

  • Yasushi Umeda (a1), Masaki Ishii (a1), Masaharu Yoshioka (a1), Yoshiki Shimomura (a2) and Tetsuo Tomiyama (a1)...

The relative significance of conceptual design to basic design or detail design is widely recognized, due to its influential roles in determining the product's fundamental features and development costs. Although there are some general methodologies dealing with functions in design, virtually no commercial CAD systems can support functional design, in particular so-called synthetic phase of design. Supporting the synthetic phase of conceptual design is one of the crucial issues of CAD systems with function modeling capabilities. In this paper, we propose a computer tool called a Function-Behavior-State (FBS) Modeler to support functional design not only in the analytical phase but also in the synthetic phase. To do so, the functional decomposition knowledge and physical features in the knowledge base of the modeler, and a subsystem Qualitative Process Abduction System (QPAS) play crucial roles. Modeling scheme of function in relation with behavior and structure and design process for conceptual design in the FBS Modeler are described. The advantages of the FBS Modeler are demonstrated by presenting two examples; namely, an experiment in which designers used this tool and the design of functionally redundant machines, which is a new design methodology for highly reliable machines, as its application.

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A. Abu-Hanna , R. Benjamins , & W. Jansweijer (1991). Device understanding and modeling for diagnosis. IEEE Expert 6(2), 2632.

J.A. Bradshaw , & R.M. Young (1991). Evaluating design using knowledge of purpose and knowledge of structure. IEEE Expert 6 (2), 3340.

K. Forbus (1984). Qualitative process theory. Artif. Intell. 24(3), 85168.

D.W. Franke (1991). Deriving and using descriptions of purpose. IEEE Expert 6(2), 4147.

A.M. Keuneke (1991). Device representation: The significance of functional knowledge. IEEE Expert 6 (2), 2225.

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  • ISSN: 0890-0604
  • EISSN: 1469-1760
  • URL: /core/journals/ai-edam
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