Research on Shape Feature Recognition of B-Rep Model Based on Wavelet Transform

Wang, Jihua

doi:10.1155/2018/6310482

Cited by 2 publications

(3 citation statements)

References 21 publications

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“…The former uses Deep 3D Convolutional Neural Networks (3D-CNNs) termed FeatureNet to learn machining feature from CAD models of mechanical parts, and the latter adopts Multiple Sectional View (MSV) representation for feature recognition. Gao et al [28] and Wang [29] employed optimization algorithm, such as ant colony searching algorithm and wavelet transform of surface boundary, to search the optimal face matching sequence between the two models. Kim et al [30] and Bespalov et al [31] suggested volume decomposition methods for feature recognition.…”

Section: Other Feature Recognition Methodsmentioning

confidence: 99%

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

et al. 2021

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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.INDEX TERMS Automatic feature recognition, similarity evaluation, multi-dimensional attributed adjacency matrix, weighted complete bipartite graph, Kuhn-Munkres algorithm.

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Section: Other Feature Recognition Methodsmentioning

confidence: 99%

“…According to the definition of bipartite graph in Formula (29), as well as the preceding definition of edge weights on model faces, a weighted complete bipartite graph is established, as shown in Fig. 4.…”

Section: A Construction Of Weighted Complete Bipartite Graphmentioning

confidence: 99%

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

et al. 2021

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“…All the same, the generation of the part geometry's STEP knowledge base plays an important role in understanding the dimensionality of the part. Primarily, the manufacturing machines consider the part's dimensions to a given machine's workspace [10]. This requires the part dimensions of the each-sided feature.…”

Section: Review Of Literature Surveymentioning

confidence: 99%

Communication of Design Data in Manufacturing Democratization

Ghorpade

Raman

2023

JMMP

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Part design is the principal source of communicating design intent to manufacturing and inspection. Design data are often communicated through computer-aided design (CAD) systems. Modern analytics tools and artificial intelligence integration into manufacturing have significantly advanced machine recognition of design specification and manufacturing constraints. These algorithms require data to be uniformly structured and easily consumable; however, the design data are represented in a graphical structure and contain a nonuniform structure, which limits the use of machine learning algorithms for a variety of tasks. This paper proposes an algorithm for extracting dimensional data from three-dimensional (3D) part designs in a structured manner. The algorithm extracts face dimensions and their relationships with other faces, enabling the recognition of underlying patterns and expanding the applicability of machine learning for various tasks. The extracted part dimensions can be stored in a dimension-based numeric extensible markup language (XML) file, allowing for easy storage and use in machine-readable formats. The resulting XML file provides a dimensional representation of the part data based on their features. The proposed algorithm reads and extracts dimensions with respect to each face of the part design, preserving the dimensional and face relevance. The uniform structure of the design data facilitates the processing of data by machine learning algorithms, enabling the detection of hidden patterns and the development of pattern-based predictive algorithms.

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Research on Shape Feature Recognition of B-Rep Model Based on Wavelet Transform

Cited by 2 publications

References 21 publications

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

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

Communication of Design Data in Manufacturing Democratization

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