Design candidate identification using neural network-based fuzzy reasoning

Abstract

Conceptual design has profound impact on success of a product design. Identification of the best conceptual design candidate is a crucial step as design information is not complete and design knowledge is minimal at conceptual design stage. This paper presents a method for design candidate evaluation and identification using neural network-based fuzzy reasoning. The method consists of the following 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 using neural network-based fuzzy reasoning. A case study is given to show the effectiveness of the proposed method and associated algorithms.