Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-27T07:03:18.108Z Has data issue: false hasContentIssue false
  • English
  • Français

DEMOCRATISING DESIGN THROUGH SURROGATE MODEL CONVOLUTIONAL NEURAL NETWORKS OF COMPUTER AIDED DESIGN REPOSITORIES

Part of: Design Innovation, Information and Knowledge

Published online by Cambridge University Press:  11 June 2020

J. Gopsill  and
S. Jennings
J. Gopsill*
Affiliation:
University of Bath, United Kingdom
S. Jennings
Affiliation:
University of Bath, United Kingdom
*
*J.A.Gopsill@bath.ac.uk

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.

The capability to manufacture at home is continually increasing with technologies, such as 3D printing. However, the ability to design products suitable for manufacture and use remains a highly-skilled and knowledge intensive activity. This has led to ‘content creators’ providing vast repositories of manufacturable products for society, however challenges remain in the search & retrieval of models. This paper presents the surrogate model convolutional neural networks approach to search and retrieve CAD models by mapping them directly to their real-world photographed counterparts.

Keywords

3D printingcomputer-aided design (CAD)design informaticssurrogate modelsconvolutional neural networks
Type
Article
Information
Proceedings of the Design Society: DESIGN Conference , Volume 1 , May 2020 , pp. 1285 - 1294
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), 2020. Published by Cambridge University Press

References

Alcock, C., Hudson, N. and Chilana, P.K. (2016), “Barriers to using, customizing, and printing 3D designs on thingiverse”, Proceedings of the 19th international conference on supporting group work, pp. 195–199.CrossRefGoogle Scholar
Amagliani, D. (2018), Toy car model. Retrieved from August 2, 2019, https://grabcad.com/library/wooden-toy-car-10Google Scholar
Anon. (n.d.), Arc 3d webservice. Retrieved from September 16, 2019, https://homes.esat.kuleuven.be/∼visit3d/webservice/v2/contact.phpGoogle Scholar
Baumann, F.W. and Roller, D. (2018), “Thingiverse: Review and analysis of available files”, International Journal of Rapid Manufacturing, Vol. 7 No. 1, pp. 83–99.CrossRefGoogle Scholar
Bourque, M. (2018), Coffee cup model. Retrieved from August 2, 2019, https://grabcad.com/library/coffee-cup-59Google Scholar
Buehler, E. et al. (2015), “Sharing is caring: Assistive technology designs on thingiverse”, In Proceedings of the 33rd annual acm conference on human factors in computing systems, pp. 525–534.CrossRefGoogle Scholar
Furukawa, Y. and Ponce, J. (2009), “Accurate, dense, and robust multiview stereopsis”, IEEE transactions on pattern analysis and machine intelligence, Vol. 32 No. 8, pp. 1362–1376.CrossRefGoogle Scholar
He, K. et al. (2015), “Deep residual learning for image recognition”, arXiv: 1512.03385Google Scholar
Hermans, G. (2015), Opening up design: Engaging the layperson in the design of everyday products (Doctoral dissertation, Umea˚ Institute of Design, Faculty of Science and Technology, Umea˚ University).Google Scholar
Jha, A.K. and Gurumoorthy, B. (2014), “Reconstruction of 3D geometry of general curved surfaces from a single image”, Computer-Aided Design and Applications, Vol. 11 No. 5, pp. 589–596.CrossRefGoogle Scholar
Karlie, S. (2018), Spur gear model. Retrieved from August 2, 2019, https://grabcad.com/library/spur-gear-18Google Scholar
Katholieke Universiteit Leuven Visics Group (2011), ARC3D Webservice 2011. A Family of Web Tools for Remote 3D Reconstruction. [Online] Viewed 12th February 2019, Accessible: https://homes.esat.kuleuven.be/∼visit3d/webservice/v2/contact.phpGoogle Scholar
Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012), “Imagenet classification with deep convolutional neural networks”, Advances in neural information processing systems, Vol. 25, pp. 1097–1105, Curran Associates, Inc.Google Scholar
Lee, Y.T. and Fang, F. (2011), “3d reconstruction of polyhedral objects from single parallel projections using cubic corner”, Computer-Aided Design, Vol. 43 No. 8, pp. 1025–1034.CrossRefGoogle Scholar
Liu, Y.-J., Tang, K. and Joneja, A. (2005), “Sketch-based free-form shape modelling with a fast and stable numerical engine”, Computers & Graphics, Vol. 29 No. 5, pp. 771–786.CrossRefGoogle Scholar
Makerbot Industries, 2015. Makerbot Digitiser. Makerbot Industries, [Online] Viewed 10th February 2019, Accessible: https://pages.makerbot.com/ap-makerbot-digitizer.htmlGoogle Scholar
Maturana, D. and Scherer, S. (2015), “Voxnet: A 3d convolutional neural network for real-time object recognition”, In 2015 ieee/rsj international conference on intelligent robots and systems (iros), IEEE, pp. 922–928.CrossRefGoogle Scholar
Mikoajczyk, A. and Grochowski, M. (2018), “Data augmentation for improving deep learning in image classification problem”, In 2018 international interdisciplinary phd workshop (iiphdw), pp. 117–122.CrossRefGoogle Scholar
Patil, D. (2018), Computer mouse model. Retrieved from August 2, 2019, https://grabcad.com/library/mouse-325Google Scholar
Solanki, K. (2019), Turbine model. Retrieved from August 2, 2019, https://grabcad.com/library/impeller-233Google Scholar
Szegedy, C. et al. (2015), Going deeper with convolutions. In Computer vision and pattern recognition (cvpr).CrossRefGoogle Scholar
Szegedy, C. et al. (2015), Rethinking the inception architecture for computer vision. CoRR, abs/1512.00567. arXiv: 1512.00567Google Scholar
Tingdahl, D. and Van Gool, L. (2011), “A public system for image based 3D model generation”, In Int. conference on computer vision/computer graphics collaboration techniques and applications, Springer, pp. 262–273.CrossRefGoogle Scholar
Van den Heuvel, F.A, . (1998), “3D reconstruction from a single image using geometric constraints”, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 53 No. 6, pp. 354–368.CrossRefGoogle Scholar
Weiss-Cohen, M., Bondarenko, A. and Halevi, Y. (2009), “Reconstruction of 3D models from measurements obtained by a moving sensor”, Computer-Aided Design and Applications, Vol. 6 No. 1, pp. 103–114.CrossRefGoogle Scholar
Zaki, H.F.M. et al. (2016), “Modeling 2D appearance evolution for 3D object categorization”, In 2016 international conference on digital image computing: Techniques and applications (DICTA), IEEE.CrossRefGoogle Scholar