Abstract
Automatic Feature Recognition (AFR) is considered as the key connection technique of the integration of Computer Aided Design (CAD) and Computer Aided Process Planning (CAPP). At present, there is a lack of a systematic method to identify and evaluate the local features of 3D CAD models. The process information such as topological structure, shape and size, tolerance and surface roughness should be considered. Therefore, a novel Model Based Definition (MBD) based on 3D CAD model AFR and similarity evaluation are proposed in this paper. A Multi-Dimensional Attributed Adjacency Matrix (MDAAM) based on MBD is established based on the fully consideration of the topological structure, shape and size, surface roughness, tolerance and other process information of the B-rep model. Based on the MDAAM, a two-stage model local feature similarity evaluation method is proposed, which combines the methods of optimal matching and adjacency judgment. First, the faces of source feature and target model are used as independent sets to construct a bipartite graph. Secondly, supplement the vertices in the independent set of source feature to make the number of vertices in two independent sets equal. Thirdly, based on MDAAM data, the weighted complete bipartite graph is constructed with the face similarity between two independent sets as the weight. Fourthly, Kuhn-Munkres algorithm is used to calculate the optimal matching between the faces of source feature and target model. Fifthly, the adjacency between matching faces in target model is judged. Finally, the similarity between matching faces of the two models is calculated, which is used as the similarity evaluation result. The effectiveness of this method is verified by three applications.
Highlights
Automatic Feature Recognition (AFR) refers to the extraction of feature information with specific engineering semantics from part models
Computer Aided Design (CAD) model of part is composed of low-level graphic elements such as faces, lines, points and etc., while Computer Aided Process Planning (CAPP) information is generated based on high-level features with engineering semantics, such as holes
Based on the similarity of appearance and process, this paper proposes to address the problems of identifying similar local feature in target model and evaluating the similarity with source feature
Summary
Automatic Feature Recognition (AFR) refers to the extraction of feature information with specific engineering semantics from part models. In the process of feature recognition and similarity evaluation of work pieces, it is necessary to consider their process information such as surface roughness and tolerance of the main faces in addition to topological structure and shape size. S. Ding et al.: MBD Based 3D CAD Model Automatic Feature Recognition and Similarity Evaluation. Based on the topological structure and geometric data of the model itself, process information such as surface roughness, tolerances and annotations are added. Based on the similarity of appearance and process, this paper proposes to address the problems of identifying similar local feature in target model and evaluating the similarity with source feature. The information set of the B-rep representation of source feature and target model is extracted, and the MBD based Multi-Dimensional Attributed Adjacency Matrix (MDAAM) is constructed.
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