Integrating radial basis function networks with case-based reasoning for product design

Authors:
Sabum Jung;Taesoo Lim;Dongsoo Kim
Affiliations:
Development Research Group, LG Production Engineering Research Institute, Pyungtaek, Gyeonggi, 451-713, Republic of Korea;Department of Computer Engineering, Sungkyul University, Anyang, Gyeonggi, 430-742, Republic of Korea;Department of Industrial and Information Systems Engineering, Soongsil University, Seoul 156-743, Republic of Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

This paper presents a case-based design expert system that automatically determines the design values of a product. We focus on the design problem of a shadow mask which is a core component of monitors in the electronics industry. In case-based reasoning (CBR), it is important to retrieve similar cases and adapt them to meet design specifications exactly. Notably, difficulties in automating the adaptation process have prevented designers from being able to use design expert systems easily and efficiently. In this paper, we present a hybrid approach combining CBR and artificial neural networks in order to solve the problems occurring during the adaptation process. We first constructed a radial basis function network (RBFN) composed of representative cases created by K-means clustering. Then, the representative case most similar to the current problem was adjusted using the network. The rationale behind the proposed approach is discussed, and experimental results acquired from real shadow mask design are presented. Using the design expert system, designers can reduce design time and errors and enhance the total quality of design. Furthermore, the expert system facilitates effective sharing of design knowledge among designers.