Retrieving Assembly Part Design Using Case-Based Reasoning and Genetic Algorithms
Document Type
Conference Proceeding
Publication Date
12-1-2005
Description
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.
Citation Information
Chang, Guanghsu A.; Su, Cheng Chung; and Priest, John W.. 2005. Retrieving Assembly Part Design Using Case-Based Reasoning and Genetic Algorithms. American Society of Mechanical Engineers, Manufacturing Engineering Division, MED. Vol.16-1 547-554. https://doi.org/10.1115/IMECE2005-80334 ISBN: 0791842231,9780791842232,0791842231,9780791842232