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.
- Manufacturing Engineering Division and Materials Handling Division
Retrieving Assembly Part Design Using Case-Based Reasoning and Genetic Algorithms
- Views Icon Views
- Share Icon Share
- Search Site
Chang, GA, Su, C, & Priest, JW. "Retrieving Assembly Part Design Using Case-Based Reasoning and Genetic Algorithms." Proceedings of the ASME 2005 International Mechanical Engineering Congress and Exposition. Manufacturing Engineering and Materials Handling, Parts A and B. Orlando, Florida, USA. November 5–11, 2005. pp. 547-554. ASME. https://doi.org/10.1115/IMECE2005-80334
Download citation file: