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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

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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

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