Graph-based heuristics for recognition of machined features from a 3D solid model
Computer-Aided Design
Applying the perceptron to three-dimensional feature recognition
Applying the perceptron to three-dimensional feature recognition
Geometric algorithms for recognition of features from solid models
Geometric algorithms for recognition of features from solid models
Geometric Reasoning for Recognition of Three-Dimensional Object Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Reasoning for Recognition of Three-Dimensional Object Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial Reasoning for the Automatic Recognition of Machinable Features in Solid Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Reasoning for Extraction of Manufacturing Features in Iso-Oriented Polyhedrons
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Theory with Applications to Engineering and Computer Science (Prentice Hall Series in Automatic Computation)
Manufacturing feature recognition toward integration with processplanning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hybrid feature recognition method for setup planning from STEP AP-203
Robotics and Computer-Integrated Manufacturing
An artificial intelligence planning approach to manufacturing feature recognition
Computer-Aided Design
Collaborative intelligent CAD framework incorporating design history tracking algorithm
Computer-Aided Design
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Among the existing feature recognition approaches, graph-based and hint-based approaches are more popular. While graph-based algorithms are quite successful in recognizing isolated features, hint based approaches intrinsically show better performance in handling interacting features. In this paper, feature traces as defined by hint based approaches are implemented and represented in concave graph forms helping the recognition of interacting features with less computational effort. The concave graphs are also used to handle curved 2.5D features while many of the previous graph-based approaches have merely dealt with polyhedral features. The method begins by decomposing the part graph to generate a set of concave sub-graphs. A feature is then recognized based on the properties of the whole concave graph or the properties of its nodes. Graph-based approaches are not intrinsically suitable to provide volumetric representation for the features, but the complete boundary information of a feature can be more effectively obtained volumetrically. Therefore, in this research a method to generate feature volumes for the recognized sub-graphs is also proposed. The approach shows better recognition ability than sub-graph isomorphism methods.