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CLUSTERING OF SEQUENTIAL CAD MODELLING DATA

Published online by Cambridge University Press:  19 June 2023

Jelena Šklebar ,
Tomislav Martinec ,
Marija Majda Perišić  and
Mario Štorga
Jelena Šklebar*
Affiliation:
University of Zagreb
Tomislav Martinec
Affiliation:
University of Zagreb
Marija Majda Perišić
Affiliation:
University of Zagreb
Mario Štorga
Affiliation:
University of Zagreb
*
Šklebar, Jelena, University of Zagreb, Croatia, jelena.sklebar@fsb.hr

Abstract

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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.

Keywords

Computer Aided Design (CAD)Computational design methodsBig dataCluster analysisCAD modelling sequences
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 937 - 946
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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