The Design Manufacturing Futures Lab has published and presented a paper entitled “Capturing Mathematical and Human Perceptions of Shape and Form through Machine Learning” that extends the groups knowledge, capability and understanding of how ML could be applied to support Engineering Design. This was published and presented at the International Conference on Engineering Design 2021 (ICED21)


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.


 Machine learning, Evaluation, User centred design, shape and form, perception