Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-10T13:34:35.435Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

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

References

Bonino, B., Raffaeli, R., Monti, M. and Giannini, F. (2021), “A heuristic approach to detect CAD assembly clusters”, Procedia CIRP, Vol. 100, Elsevier B.V., pp. 463–468.CrossRefGoogle Scholar
Cantamessa, M., Montagna, F., Altavilla, S. and Casagrande-Seretti, A. (2020), “Data-driven design: The new challenges of digitalization on product design and development”, Design Science, Cambridge University PressGoogle Scholar
Celjak, R., Horvat, N. and Skec, S. (2022), “Exploring the Potential of Tracking CAD Actions in Project-based Courses”, CAD’22 Proceedings, CAD Solutions, LLC, pp. 302–307.CrossRefGoogle Scholar
Garland, M., Willmott, A. and Heckbert, P.S. (2001), “Hierarchical Face Clustering on Polygonal Surfaces”, Proceedings of the 2001 Symposium on Interactive 3D Graphics - SI3D ’01, pp. 49–58.CrossRefGoogle Scholar
Hamerly, G. and Elkan, C. (2002), “Alternatives to the k-means algorithm that find better clusterings”, Proceedings of the Eleventh International Conference on Information and Knowledge Management - CIKM ’02, pp. 600–607.Google Scholar
Han, Z., Mo, R. and Hao, L. (2019), “Clustering and retrieval of mechanical CAD assembly models based on multi-source attributes information”, Robotics and Computer-Integrated Manufacturing, Elsevier Ltd, Vol. 58, pp. 220–229.CrossRefGoogle Scholar
Hoffmann, C.M. (1989), Geometric and Solid Modeling: An Introduction, 1st ed., Morgan Kaufmann Pub, San Mateo, California.Google Scholar
Chen, James Yu-Hsien, by, Olechowski, A. and Yu-Hsien Chen, J. (2021), Development of a Novel Computer-Aided Design Experiment Protocol for Studying Designer Behaviours, University of Toronto.Google Scholar
Jayaraman, P.K., Lambourne, J.G., Desai, N., Willis, K.D.D., Sanghi, A. and Morris, N.J.W. (2022), “SolidGen: An Autoregressive Model for Direct B-rep Synthesis”Google Scholar
Jollife, I.T. and Cadima, J. (2016), “Principal component analysis: A review and recent developments”, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Royal Society of London, 13 AprilGoogle Scholar
Katz, S. and Tal, A. (2003), “Hierarchical Mesh Decomposition using Fuzzy Clustering and Cuts”, SIGGRAPH ’03, pp. 954–961.CrossRefGoogle Scholar
Li, C., Pan, H., Bousseau, A. and Mitra, N.J. (2020), “Sketch2CAD: Sequential CAD modeling by sketching in context”, ACM Transactions on Graphics, Association for Computing Machinery, Vol. 39 No. 6Google Scholar
Machchhar, R.J. and Bertoni, A. (2021), “Data-driven design automation for product-service systems design: Framework and lessons learned from empirical studies”, Proceedings of the Design Society, Vol. 1, Cambridge University Press, pp. 841–850.Google Scholar
Murtagh, F. and Contreras, P. (2012), “Algorithms for hierarchical clustering: an overview”, WIREs Data Mining and Knowledge Discovery, Vol. 2 No. 1, pp. 86–97.CrossRefGoogle Scholar
Omran, M.G.H., Engelbrecht, A.P. and Salman, A. (2007), “An overview of clustering methods”, Intelligent Data Analysis, IOS Press, Vol. 11 No. 6, pp. 583–605.Google Scholar
Onshape. (n.d.). “Developer Portal API”, available at: https://onshape-public.github.io/docs/ (accessed 4 December 2022).Google Scholar
Oyelade, J., Isewon, I., Oladipupo, O., Emebo, O., Omogbadegun, Z., Aromolaran, O., Uwoghiren, E., et al. (2019), “Data Clustering: Algorithms and Its Applications”, Proceedings - 2019 19th International Conference on Computational Science and Its Applications, ICCSA 2019, Institute of Electrical and Electronics Engineers Inc., pp. 71–81.CrossRefGoogle Scholar
Peabody, M. and Regli, C., W. (2001), Clustering Techniques for Databases of CAD Models, Philadelphia.Google Scholar
Pedley, A.G. (1997), User Defined Feature Modelling: Representing Extrinsic Form, Dimensions And Tolerances.Google Scholar
Rahman, M.H., Schimpf, C., Xie, C. and Sha, Z. (2019), “A computer-aided design based research platform for design thinking studies”, Journal of Mechanical Design, Transactions of the ASME, American Society of Mechanical Engineers (ASME), Vol. 141 No. 12Google Scholar
Regli, W.C. (1995), Geometric Algorithms for Recognition of Features from Solid Models.Google Scholar
Roj, R. and Woyand, H.-B. (2015), An Examination of Engineering Parts in Large CAD-Databases in Order to Create Adjacency Matrices and Build Clusters.CrossRefGoogle Scholar
Salomons, O.W., van Houten, F.J.A.M. and Kals, H.J.J. (1993), Review of Research in Feature-Based Design, Journal of Manufacturing Systems, Vol. 12.CrossRefGoogle Scholar
Sharma, G., Goyal, R., Liu, D., Kalogerakis, E. and Maji, S. (2017), “CSGNet: Neural Shape Parser for Constructive Solid Geometry”CrossRefGoogle Scholar
Shi, C., Wei, B., Wei, S., Wang, W., Liu, H. and Liu, J. (2021), “A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm”, Eurasip Journal on Wireless Communications and Networking, Springer Science and Business Media Deutschland GmbH, Vol. 2021 No. 1Google Scholar
Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H. and Sui, F. (2018), “Digital twin-driven product design, manufacturing and service with big data”, International Journal of Advanced Manufacturing Technology, Springer London, Vol. 94 No. 9–12, pp. 3563–3576.CrossRefGoogle Scholar
Uy, M.A., Chang, Y., Sung, M., Goel, P., Lambourne, J., Birdal, T. and Guibas, L. (2021), “Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinders”CrossRefGoogle Scholar
Vasantha, G., Purves, D., Quigley, J., Corney, J., Sherlock, A. and Randika, G. (2021), “Common design structures and substitutable feature discovery in CAD databases”, Advanced Engineering Informatics, Elsevier Ltd, Vol. 48Google Scholar
Willis, K.D.D., Pu, Y., Luo, J., Chu, H., Du, T., Lambourne, J.G., Solar-Lezama, A., et al. (2021), “Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design Sequences”, ACM Transactions on Graphics, Association for Computing Machinery, Vol. 40 No. 4CrossRefGoogle Scholar
Wu, R., Xiao, C. and Zheng, C. (2021), “DeepCAD: A Deep Generative Network for Computer-Aided Design Models”CrossRefGoogle Scholar