An examination of engineering parts in large CAD-databases in order to create adjacency matrices and build clusters

Roj, Robin; Woyand, Hans-Bernhard

doi:10.1109/ines.2015.7329646

Cited by 2 publications

(1 citation statement)

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“…Partitioning the CAD repository into subspaces of similar CAD models is another research direction with the overall objective of facilitation and automation of design retrieval and reuse. Thus, a model signature graph (MSG) and AAG of CAD model parts used as a solid model representation constructed from B-Rep are used as input data into the clustering algorithm for CAD models recognition (Peabody et al, 2001;Roj et al H.-B., 2015). The related work regarding CAD clustering shows a lack of research on clustering the CAD modelling history data.…”

Section: Cad Models Clusteringmentioning

confidence: 99%

Clustering of Sequential Cad Modelling Data

et al. 2023

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Automating modelling activities in computer-aided design (CAD) systems is no exception within design automation, one of the current research endeavours aiming to use and transform design-related data in design decision-making processes and the generation and evaluation facilitation of new design solutions. The paper explores the differences between CAD models based on their feature-based CAD modelling sequences that lead to the final models' design. The dataset collected and structured for the study contains more than 1400 CAD models clustered on two levels by using an unsupervised K-means clustering algorithm. The algorithm is performed on the number (total and unique) and the first-order Markov model transition matrices of the CAD modelling operations and their sequential order, respectively. Therefore, three and ten groups (clusters) of CAD models are obtained regarding the level of clustering. The results show that most of the obtained groups are specified by the dominant transition between particular modelling operations. In addition, the study also provides insight into the potential of using feature-based CAD modelling operations' sequences as a first step toward automating the user interaction with the CAD system.

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Section: Cad Models Clusteringmentioning

confidence: 99%