Solid Models are the critical data elements in modern Computer-Aided Design (CAD) environments, describing 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 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 most significant in the classification of models. This paper presents our approach to manufacturing similarly assessment of solid models of mechanical parts based on machining features. Our technical approach is three-fold: (1) perform machining feature extraction; (2) construct a model dependency graph (MDG) from the set of machining features; (3) 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.
Cicirello and Regli REF examined issues pertaining to graph-based data structures and proposed a model dependency graph (MDG) approach to determine the machining features.
An approach to a feature-based comparison of solid models of machined parts
AI EDAM
AI EDAM
Computer Science