Research On The Technology Of Manufacturing Features Recognition Based On Model Definition

In order to achieve digital definition of 3D models of products, some information about manufacturing feature needs to be extracted from the CAD models, which is the key for organic integration of the CAD/CAPP/CAM system. The system of CAPP can get the information about manufacturing feature from 3D models directly with the method of feature recognition, and complete the data exchange and sharing between2 D and 3D system, in order to realize the data transformation to MBD system. To solve the disadvantages in features recognition method, the method of manufacturing feature recognition for machine parts under 3D environment is studied in this paper. The main contents are as follows:(1)Based on the definition of manufacturing features under 3D environment, the weaknesses of constructing method to construct this papers’ manufacturing features library in UG is analyzed, and the method based on secondary development for UG is put forward and used to construct user-defined manufacturing features library;(2)Judging the concavity and convexity of models’ edge is the foundation of manufacturing features recognition, after judging edges’ property, we can extract the faces of feature, resolve the attributed adjacency graph, and match the graphs. A unified algorithm for judging the concavity and convexity of models’ edge is put forward in this paper, either linear or planar curves can be judged in this algorithm,regardless of its property;(3)Traditional feature recognition method is less of efficiency, and it cannot separate features with sub-graph completely, so a manufacturing features recognitionmethod is propose. Through this method, the attribute adjacent graph of CAD models is decomposed into sub-graph with maximum extent, and matched with the manufacturing features after decomposition with the sub-graph which predefined in the library through the graph isomorphic, then the manufacturing feature can be recognized;(4)Based on the above research results, the correctness of the algorithm in this paper is demonstrated by run a program.