Abstract

This paper presents a neural network-based fuzzy reasoning method for design candidate evaluation and identification to improve design quality and efficiency at the crucial conceptual design stage. The evaluation and identification of design candidates are carried out through the following four steps: (1) acquisition of customer needs and ranking of their importance, (2) establishment of measurable metrics and their relations with customer needs, (3) development of design specifications and initial evaluation of design candidates, and (4) evaluation and identification of design candidates based on design specifications and customer needs. A case study is given to show the effectiveness of the neural network-based fuzzy reasoning method in conceptual design evaluation.

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