Automatic discovery of common design structures in CAD models

Ma, Lujie; Huang, Zhengdong; Wang, Yanwei

doi:10.1016/j.cag.2010.06.002

Cited by 35 publications

(19 citation statements)

References 16 publications

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“…Machine-based Learning tools are widely used in design activities throughout the product life cycle to address optimization problems [11], decision making problems [6,13], shapes recognition [4,9,13], item recognition and extraction for reuse, recognition from point cloud scans and reverse engineering [2].…”

Section: Importance Of the Work And Related State-of-the-artmentioning

confidence: 99%

On the use of Machine Learning to Defeature CAD Models for Simulation

Danglade¹,

Pernot²,

Véron³

2013

Computer-Aided Design and Applications

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International audienceNumerical simulations play more and more important role in product development cycles and are increasingly complex, realistic and varied. CAD models must be adapted to each simulation case to ensure the quality and reliability of the results. The defeaturing is one of the key steps for preparing digital model to a simulation. It requires a great skill and a deep expertise to foresee which features have to be preserved and which features can be simplified. This expertise is often not well developed and strongly depends of the simulation context. In this paper, we propose an approach that uses machine learning techniques to identify rules driving the defeaturing step. The expertise knowledge is supposed to be embedded in a set of configurations that form the basis to develop the processes and find the rules. For this, we propose a method to define the appropriate data models used as inputs and outputs of the learning techniques

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Section: Importance Of the Work And Related State-of-the-artmentioning

confidence: 99%

On the use of Machine Learning to Defeature CAD Models for Simulation

Danglade¹,

Pernot²,

Véron³

2013

Computer-Aided Design and Applications

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show abstract

“…Graph searches are a fundamental component in applications such as chemo-informatics [19], [26], bioinformatics [1], computer-aided design [14], computer vision [16], [18], pattern recognition, XML, social networks, World Wide Web, and software analysis. For a database consisting of the set of graphs G = {g 1 , g 2 , • • • , g n } and a given query q, there are two types of graph searches depending on the desired output:…”

Section: Introductionmentioning

confidence: 99%

Efficient Supergraph Search Using Graph Coding

Imai

Inokuchi

2020

IEICE Trans. Inf. & Syst.

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This paper proposes a method for searching for graphs in the database which are contained as subgraphs by a given query. In the proposed method, the search index does not require any knowledge of the query set or the frequent subgraph patterns. In conventional techniques, enumerating and selecting frequent subgraph patterns is computationally expensive, and the distribution of the query set must be known in advance. Subsequent changes to the query set require the frequent patterns to be selected again and the index to be reconstructed. The proposed method overcomes these difficulties through graph coding, using a tree structured index that contains infrequent subgraph patterns in the shallow part of the tree. By traversing this code tree, we are able to rapidly determine whether multiple graphs in the database contain subgraphs that match the query, producing a powerful pruning or filtering effect. Furthermore, the filtering and verification steps of the graph search can be conducted concurrently, rather than requiring separate algorithms. As the proposed method does not require the frequent subgraph patterns and the query set, it is significantly faster than previous techniques; this independence from the query set also means that there is no need to reconstruct the search index when the query set changes. A series of experiments using a real-world dataset demonstrate the efficiency of the proposed method, achieving a search speed several orders of magnitude faster than the previous best.

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“…Graph searches are a fundamental component in applications such as chemo-informatics [19], [26], bioinformatics [1], computer-aided design [14], computer vision [16], [18], pattern recognition, XML, social networks, World Wide Web, and software analysis. For a database consisting of the set of graphs G = {g 1 , g 2 , · · · , g n } and a given query q, there are two types of graph searches depending on the desired output: [7], [8], [11], [17], [22], [24] • Supergraph search: {g i ∈ G | g i ⊆ q} [4], [13], [21], [23], [25].…”

Section: Introductionmentioning

confidence: 99%

Effect of Surrounding Atmospheres on Break Arc Durations of Electrical Contacts in DC Load Conditions

Zhang

et al. 2020

IEICE Trans. Electron.

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Shun IMAI † , Nonmember and Akihiro INOKUCHI †a) , Member SUMMARY This paper proposes a method for searching for graphs in the database which are contained as subgraphs by a given query. In the proposed method, the search index does not require any knowledge of the query set or the frequent subgraph patterns. In conventional techniques, enumerating and selecting frequent subgraph patterns is computationally expensive, and the distribution of the query set must be known in advance. Subsequent changes to the query set require the frequent patterns to be selected again and the index to be reconstructed. The proposed method overcomes these difficulties through graph coding, using a tree structured index that contains infrequent subgraph patterns in the shallow part of the tree. By traversing this code tree, we are able to rapidly determine whether multiple graphs in the database contain subgraphs that match the query, producing a powerful pruning or filtering effect. Furthermore, the filtering and verification steps of the graph search can be conducted concurrently, rather than requiring separate algorithms. As the proposed method does not require the frequent subgraph patterns and the query set, it is significantly faster than previous techniques; this independence from the query set also means that there is no need to reconstruct the search index when the query set changes. A series of experiments using a real-world dataset demonstrate the efficiency of the proposed method, achieving a search speed several orders of magnitude faster than the previous best.

show abstract

Automatic discovery of common design structures in CAD models

Cited by 35 publications

References 16 publications

On the use of Machine Learning to Defeature CAD Models for Simulation

On the use of Machine Learning to Defeature CAD Models for Simulation

Efficient Supergraph Search Using Graph Coding

Effect of Surrounding Atmospheres on Break Arc Durations of Electrical Contacts in DC Load Conditions

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