Applying a hybrid approach based on fuzzy neural network and genetic algorithm to product form design

When generating new design concepts, most industrial designers tend to draw upon stereotypical images and their own personal design experiences. The evaluation of each individual design candidate in terms of its ability to meet the demands of the marketplace is a crucial step within the conceptual design stage. Consequently, this paper proposes a method which enables an automatic product form search or product image evaluation by means of fuzzy neural network and genetic algorithm. Initially, a feature-based hierarchical computer-aided design (CAD) model is constructed, in which the related form parameters are thoroughly defined in applicable domains to facilitate the automatic generation of new product forms. A fuzzy neural network algorithm is then applied to establish the relationships between the input form parameters and a series of adjectival image words. In a reverse process, genetic algorithm is employed to search for a near-optimal design which satisfies the designer's required product image by using the trained neural network as a fitness function. The proposed method provides an automatic design system, which gives designers the ability to rapidly obtain a product form and its corresponding image, or to search for the ideal form which fits a required image in a shorter lead-time. An electronic door lock design is chosen as the subject of the current investigation. However, the proposed method is equally applicable to the design of other products. Relevance to industry At the conceptual design stage, it is very important that an enterprise creates a product form which meets the consumers' psychological preferences if that product is to be successful within the marketplace. The automatic design system presented in this study is intended to assist enterprises in achieving this objective by integrating fuzzy neural network and genetic algorithm to enable industrial designers to create required product form(s). (c) 2004 Elsevier B.V. All rights reserved.