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DESIGN DESCRIPTIONS IN THE DEVELOPMENT OF MACHINE LEARNING BASED DESIGN TOOLS

Published online by Cambridge University Press:  19 June 2023

Alison McKay ,
Thomas A Hazlehurst ,
Alan de Pennington  and
David C Hogg
Alison McKay*
Affiliation:
University of Leeds
Thomas A Hazlehurst
Affiliation:
University of Leeds
Alan de Pennington
Affiliation:
University of Leeds
David C Hogg
Affiliation:
University of Leeds
*
McKay, Alison, University of Leeds, United Kingdom, [email protected]

Abstract

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Applications of machine learning technologies are becoming ubiquitous in many sectors and their impacts, both positive and negative, are widely reported. As a result, there is substantial interest from the engineering community to integrate machine learning technologies into design workflows with a view to improving the performance of the product development process. In essence, machine learning technologies are thought to have the potential to underpin future generations of data-enabled engineering design system that will deliver radical improvements to product development and so organisational performance. In this paper we report learning from experiments where we applied machine learning to two shape-based design challenges: in a given collection of designed shapes, clustering (i) visually similar shapes and (ii) shapes that are likely to be manufactured using the same primary process. Both challenges were identified with our industry partners and are embodied in a design case study. We report early results and conclude with issues for design descriptions that need to be addressed if the full potential of machine learning is to be realised in engineering design.

Keywords

Big dataArtificial intelligenceDesign informatics
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 1227 - 1236
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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