Over 70% of final product costs are determined by initial product design. Hence, to depict a capable and reliable part design is important during the part conceptual design phase. Case-Based Reasoning (CBR), based on analogical reasoning, is a problem solving methodology. This paper proposes the CBR architecture applied to assembly part design for effectively managing previous design experience and evaluating assemblability and manufacturability of assembly part design. The results can provide a useful reference for novices to reuse and revise previous experience and experienced designers to impart their expertise through CBR methodology.

Artificial intelligence (AI) approaches have been successfully applied to many fields. Among the numerous AI approaches, Case-Based Reasoning (CBR) is an approach that mainly focuses on the reuse of knowledge and experience. However, little work is done on applications of CBR to improve assembly part design. Similarity measures and the weight of different features are crucial in determining the accuracy of retrieving cases from the case base. To develop the weight of part features and retrieve a similar part design, the research proposes using Genetic Algorithms (GAs) to learn the optimum feature weight and employing nearest-neighbor technique to measure the similarity of assembly part design. Early experimental results indicate that the similar part design is effectively retrieved by these similarity measures.

Many conflicting issues exist between product design and manufacturing department. In the early design stage, designers often do not have enough expertise to successfully address all these issues. This results in a product design with a low level of assemblability and manufacturability. Hence, an intelligent decision support system is needed for early design stages to improve a design. This paper proposed a web-based intelligent decision support system, CBR-DFMA, connecting with a case base, database and knowledge base. Early experimental results indicate that potential design problems can be detected in advance, design expertise can be effectively disseminated and effective training is offered to designer by employing this system.