Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 16(5): 385-399, . doi:10.1017/S0890060402165048



Solid models are the critical data elements in modern computer-aided design environments, because they describe the shape and form of manufactured artifacts. Their growing ubiquity has created new problems in how to effectively manage the many models that are now stored in the digital libraries for large design and manufacturing enterprises. Existing techniques from the engineering literature and industrial practice, such as group technology, rely on human-supervised encodings and classification; techniques from the multimedia database and computer graphics/vision communities often ignore the manufacturing attributes that are most significant in the classification of models. This paper presents our approach to comparing the manufacturing similarity assessments of solid models of mechanical parts based on machining features. Our technical approach is threefold: perform machining feature extraction, construct a model dependency graph (MDG) from the set of machining features, and partition the models in a database using a measure of similarity based on the MDGs. We introduce two heuristic search techniques for comparing MDGs and present empirical experiments to validate our approach using our testbed, the National Design Repository.