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A KNOWLEDGE-BASED IDEATION APPROACH FOR BIO-INSPIRED DESIGN

Published online by Cambridge University Press:  19 June 2023

Liuqing Chen ,
Zebin Cai ,
Zhaojun Jiang ,
Qi Long ,
Lingyun Sun ,
Peter Childs  and
Haoyu Zuo
Liuqing Chen
Affiliation:
Department of Computer Science and Technology, Zhejiang University, Hangzhou 310030, China; Singapore Innovation and AI Joint Research Lab, Zhejiang University, Hangzhou 310030, China;
Zebin Cai
Affiliation:
Department of Computer Science and Technology, Zhejiang University, Hangzhou 310030, China;
Zhaojun Jiang
Affiliation:
School of mechanical engineering, Tianjin University, Tianjin 300350, China;
Qi Long
Affiliation:
Zhejiang University-University of Illinois at Urbana-Champaign Institute, Haining 314400, China;
Lingyun Sun
Affiliation:
Department of Computer Science and Technology, Zhejiang University, Hangzhou 310030, China; Singapore Innovation and AI Joint Research Lab, Zhejiang University, Hangzhou 310030, China;
Peter Childs
Affiliation:
Dyson School of Design Engineering, Imperial College London, London SW7 2AZ, UK
Haoyu Zuo*
Affiliation:
Dyson School of Design Engineering, Imperial College London, London SW7 2AZ, UK
*
Zuo, Haoyu, Imperial College London, United Kingdom, [email protected]

Abstract

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Bio-inspired design (BID) involves generating innovative ideas for engineering design by drawing inspiration from natural biological phenomena and systems, using a form of design-by-analogy. Despite its many successes, BID approaches encounter research challenges including unstructured data and existing models that hinder comprehension and processing, limited focus on finding biological knowledge compared to defined problems, and insufficient guidance of the ideation process with algorithms. This paper proposes a knowledge-based approach to address the challenges. The approach involves transforming unstructured data into structured knowledge, including information about natural sources, their benefits, and applications. The structured knowledge is then used to construct a semantic network, enabling designers to retrieve information for BID in two ways. Furthermore, a three-step ideation method is developed to encourage divergent thinking and explore additional potential solutions by drawing inspiration and utilizing knowledge. The knowledge-based BID approach is implemented as a tool and design cases are conducted to illustrate the process of applying this tool for BID.

Keywords

Bio-inspired design / biomimeticsBig dataDesign methodsSemantic networkData-driven-design
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 231 - 240
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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