MBD Based 3D CAD Model Automatic Feature Recognition and Similarity Evaluation

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...

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Autores principales:	Shuhui Ding, Qiang Feng, Zhaoyang Sun, Fai Ma
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Automatic feature recognition similarity evaluation multi-dimensional attributed adjacency matrix weighted complete bipartite graph Kuhn-Munkres algorithm Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/aa251098bdaf4d139b002a5d4b2712e8
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id	oai:doaj.org-article:aa251098bdaf4d139b002a5d4b2712e8
record_format	dspace
spelling	oai:doaj.org-article:aa251098bdaf4d139b002a5d4b2712e82021-11-18T00:09:12ZMBD Based 3D CAD Model Automatic Feature Recognition and Similarity Evaluation2169-353610.1109/ACCESS.2021.3126333https://doaj.org/article/aa251098bdaf4d139b002a5d4b2712e82021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9606673/https://doaj.org/toc/2169-3536Automatic 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.Shuhui DingQiang FengZhaoyang SunFai MaIEEEarticleAutomatic feature recognitionsimilarity evaluationmulti-dimensional attributed adjacency matrixweighted complete bipartite graphKuhn-Munkres algorithmElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 150403-150425 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Automatic feature recognition similarity evaluation multi-dimensional attributed adjacency matrix weighted complete bipartite graph Kuhn-Munkres algorithm Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Automatic feature recognition similarity evaluation multi-dimensional attributed adjacency matrix weighted complete bipartite graph Kuhn-Munkres algorithm Electrical engineering. Electronics. Nuclear engineering TK1-9971 Shuhui Ding Qiang Feng Zhaoyang Sun Fai Ma MBD Based 3D CAD Model Automatic Feature Recognition and Similarity Evaluation
description	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.
format	article
author	Shuhui Ding Qiang Feng Zhaoyang Sun Fai Ma
author_facet	Shuhui Ding Qiang Feng Zhaoyang Sun Fai Ma
author_sort	Shuhui Ding
title	MBD Based 3D CAD Model Automatic Feature Recognition and Similarity Evaluation
title_short	MBD Based 3D CAD Model Automatic Feature Recognition and Similarity Evaluation
title_full	MBD Based 3D CAD Model Automatic Feature Recognition and Similarity Evaluation
title_fullStr	MBD Based 3D CAD Model Automatic Feature Recognition and Similarity Evaluation
title_full_unstemmed	MBD Based 3D CAD Model Automatic Feature Recognition and Similarity Evaluation
title_sort	mbd based 3d cad model automatic feature recognition and similarity evaluation
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/aa251098bdaf4d139b002a5d4b2712e8
work_keys_str_mv	AT shuhuiding mbdbased3dcadmodelautomaticfeaturerecognitionandsimilarityevaluation AT qiangfeng mbdbased3dcadmodelautomaticfeaturerecognitionandsimilarityevaluation AT zhaoyangsun mbdbased3dcadmodelautomaticfeaturerecognitionandsimilarityevaluation AT faima mbdbased3dcadmodelautomaticfeaturerecognitionandsimilarityevaluation
_version_	1718425244569436160

MBD Based 3D CAD Model Automatic Feature Recognition and Similarity Evaluation

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