Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-26T07:47:56.806Z Has data issue: false hasContentIssue false
  • English
  • Français

CAPTURING MATHEMATICAL AND HUMAN PERCEPTIONS OF SHAPE AND FORM THROUGH MACHINE LEARNING

Published online by Cambridge University Press:  27 July 2021

James Gopsill ,
Mark Goudswaard ,
David Jones  and
Ben Hicks
James Gopsill*
Affiliation:
Design Manufacturing Futures Lab, School of Civil, Aerospace and Mechanical Engineering, University of Bristol, UK Centre for Modelling and Simulation, Bristol, UK
Mark Goudswaard
Affiliation:
Design Manufacturing Futures Lab, School of Civil, Aerospace and Mechanical Engineering, University of Bristol, UK
David Jones
Affiliation:
Design Manufacturing Futures Lab, School of Civil, Aerospace and Mechanical Engineering, University of Bristol, UK
Ben Hicks
Affiliation:
Design Manufacturing Futures Lab, School of Civil, Aerospace and Mechanical Engineering, University of Bristol, UK
*
Gopsill, James, University of Bristol Mechanical Engineering, United Kingdom, [email protected]

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.

Classifying shape and form is a core feature of Engineering Design and one that we do this instinctively on a daily basis. Matching similar components to then reduce unique component counts, determining whether a competitors design infringes on copyright and receiving market feedback on product styling are all examples where shape and form comes into play. However, shape and form can be perceived in different ways from purely mathematical (e.g. shape grammars) to wholly subjective (e.g. market feedback) and these perceptions may not entirely agree.

This paper examines the mathematical and human perceptions of shape and form through a study of classifying shapes that have been interpolated between one another, and in doing so, highlights the disparity in perceptions. Following this, the paper demonstrates how the emergent field of Machine Learning can be applied to capture mathematical and human perceptions of shape and form resulting in a means to twin this feedback into product development.

Keywords

Machine learningEvaluationUser centred designshape and formperception
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 591 - 600
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), 2021. Published by Cambridge University Press

References

Abadi, M., Agarwal, A., Barham, P., Brevdo, , et al. , 2016. Tensorflow: large-scale machine learning on heterogeneous distributed systems. Arxiv preprint arxiv:1603.04467.Google Scholar
Burnap, A., Hartley, J., Pan, Y., Gonzalez, R., and Papalambros, P.Y., 2016. Balancing design freedom and brand recognition in the evolution of automotive brand styling. Design science, 2, e9.CrossRefGoogle Scholar
Escher, M., 1989. Escher on Escher: Exploring the infinite. Abrams.Google Scholar
Fleiss, J.L. and Cohen, J., 1973. The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability. Educational and psychological measurement, 33(3), pp.613–619.CrossRefGoogle Scholar
Gopsill, J. and Jennings, S., 2020. Democratising design through surrogate model convolutional neural networks of computer aided design repositories. Proceedings of the design society: design conference, 1, pp.1285–1294. Available from: https://doi.org/10.1017/dsd.2020.93.CrossRefGoogle Scholar
Kittidecha, C. and Marasinghe, A.C., 2015. Application of kansei engineering and box-behnken response surface methodology for shape parameter design: a case study of wine glass. Journal of advanced mechanical design, systems, and manufacturing, 9(5), JAMDSM0059-JAMDSM0059.CrossRefGoogle Scholar
Landis, J.R. and Koch, G.G., 1977. The measurement of observer agreement for categorical data. Biometrics, pp.159–174.CrossRefGoogle Scholar
Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., and Talwalkar, A., 2017. Hyperband: a novel bandit-based approach to hyperparameter optimization. The journal of machine learning research, 18(1), pp.6765–6816.Google Scholar
Majoral, X., Becattini, N., O'Hare, J., Bellucci, G., Boujut, J.-F., and Martens, P., 2018. D5.3 demonstration with other creative industries and with customers [Online]. Available from: http://spark-project.net/sites/default/files/file-wp/D5.3_WP5_DEMONSTRATION_WITH_OTHER_CREATIVE_INDUSTRIES_AND_WITH_CUSTOMERS.pdf.Google Scholar
Maturana, D. and Scherer, S., 2015. Voxnet: a 3d convolutional neural network for real-time object recognition. 2015 ieee/rsj international conference on intelligent robots and systems (iros). IEEE, pp. 922–928.Google Scholar
Ranscombe, C., Hicks, B., and Mullineux, G., 2012a. A method for exploring similarities and visual references to brand in the appearance of mature mass-market products. Design studies [Online], 33(5), pp. 496–520. Available from: https://doi.org/https://doi.org/10.1016/j.destud.2012.04.001.CrossRefGoogle Scholar
Ranscombe, C., Hicks, B., Mullineux, G., and Singh, B., 2012b. Visually decomposing vehicle images: exploring the influence of different aesthetic features on consumer perception of brand. Design studies [Online], 33(4), pp.319–341. Available from: https://doi.org/10.1016/j.destud.2011.06.006.CrossRefGoogle Scholar
Real, R., Gopsill, J., Jones, D., Snider, C., and Hicks, B., 2021. Distinguishing artefacts: evaluating the saturation point of convolutional neural networks. Cirp design conference. In press.CrossRefGoogle Scholar
Venturi, F., Andrich, G., Sanmartin, C., Taglieri, I., Scalabrelli, G., Ferroni, G., and Zinnai, A., 2016. Glass and wine: a good example of the deep relationship between drinkware and beverage. Journal of wine research, 27(2), pp.153–171.CrossRefGoogle Scholar
Zaki, H.F., Shafait, F., and Mian, A., 2016. Modeling 2d appearance evolution for 3d object categorization. 2016 international conference on digital image computing: techniques and applications (dicta). IEEE, pp. 1–8.Google Scholar