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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

References

Chen, L., Wang, P., Dong, H., Shi, F., Han, J., Guo, Y., Childs, P.R., Xiao, J. and Wu, C., 2019. An artificial intelligence based data-driven approach for design ideation. Journal of Visual Communication and Image Representation, Vol. 61, pp.10–22. https://doi.org/10.1016/j.jvcir.2019.02.009CrossRefGoogle Scholar
Childs, P., Han, J., Chen, L., Jiang, P., Wang, P., Park, D., Yin, Y., Dieckmann, E. and Vilanova, I., 2022. The creativity diamond—a framework to aid creativity. Journal of Intelligence, Vol. 10, p.73. https://doi.org/10.3390/jintelligence10040073CrossRefGoogle ScholarPubMed
Chirazi, J., Wanieck, K., Fayemi, P.-E., Zollfrank, C. & Jacobs, S. 2019. What Do We Learn From Good Practices Of Biologically Inspired Design In Innovation? Applied Sciences, Vol. 9, p. 650. https://doi.org/10.3390/app9040650CrossRefGoogle Scholar
Cohen, Y. H. & Reich, Y. 2016. Biomimetic design method for innovation and sustainability (Vol. 10). Berlin, Germany. Springer. https://doi.org/10.1007/978-3-319-33997-9CrossRefGoogle Scholar
Deldin, J.-M. & Schuknecht, M. 2014. The Asknature Database: Enabling Solutions In Biomimetic Design. Biologically Inspired Design. Springer. https://doi.org/10.1007/978-1-4471-5248-4_2CrossRefGoogle Scholar
Fayemi, P.e., Wanieck, K., Zollfrank, C., Maranzana, N., Aoussat, A., 2017. Biomimetics: Process, Tools And Practice. Bioinspir. Biomim. Vol. 12, p. 011002. https://doi.org/10.1088/1748-3190/12/1/011002CrossRefGoogle Scholar
Fu, K., Chan, J., Cagan, J., Kotovsky, K., Schunn, C. & Wood, K. 2013. The Meaning Of “Near” And “Far”: The Impact Of Structuring Design Databases And The Effect Of Distance Of Analogy On Design Output. Journal Of Mechanical Design, Vol. 135. https://doi.org/10.1115/1.4023158CrossRefGoogle Scholar
Fu, K., Moreno, D., Yang, M. & Wood, K. L. 2014. Bio-inspired Design: An Overview Investigating Open Questions From The Broader Field Of Design-by-analogy. Journal Of Mechanical Design, Vol. 136, p. 111102. https://doi.org/10.1115/1.4028289CrossRefGoogle Scholar
Goel, A., Hagopian, K., Zhang, S. & Rugaber, S. Towards A Virtual Librarian For Biologically Inspired Design. Design Computing and Cognition, Cham. Springer International Publishing, pp. 369–386. https://doi.org/10.1007/978-3-030-90625-2_21CrossRefGoogle Scholar
Goel, A. K., Mcadams, D. A. & Stone, R. B. 2015. Biologically Inspired Design, Springer.CrossRefGoogle Scholar
Han, J., Sarica, S., Shi, F., Luo, J., 2021. Semantic Networks For Engineering Design: State Of The Art And Future Directions. J. Mech. Des. pp. 1–45. https://doi.org/10.1115/1.4052148CrossRefGoogle Scholar
Cohen, Helfman, Reich, Y., & Greenberg, Y., S. 2014. Biomimetics: Structure–function Patterns Approach. Journal Of Mechanical Design, Vol. 136, p. 111108. https://doi.org/10.1115/1.4028169CrossRefGoogle Scholar
Hooker, G. & Smith, E. 2016. Asknature And The Biomimicry Taxonomy. Insight, Vol. 19, pp. 46–49. https://doi.org/10.1002/inst.12073CrossRefGoogle Scholar
Jacobs, S. R., Nichol, E. C. & Helms, M. E. 2014. “Where Are We Now And Where Are We Going?” The Biom Innovation Database. Journal Of Mechanical Design, Vol. 136, p. 111101. https://doi.org/10.1115/1.4028171CrossRefGoogle Scholar
Jiang, S., Hu, J., Wood, K. L. & Luo, J. 2021. Data-driven Design-by-analogy: State-of-the-art And Future Directions. Journal Of Mechanical Design, Vol. 144. https://doi.org/10.1115/1.4051681Google Scholar
Kozaki, K. & Mizoguchi, R., 2014. pp. 469–472.Google Scholar
Linsey, J. S., Tseng, I., Fu, K., Cagan, J., Wood, K. L. & Schunn, C. 2010. A Study Of Design Fixation, Its Mitigation And Perception In Engineering Design Faculty. Journal Of Mechanical Design, Vol. 132. https://doi.org/10.1115/1.4001110CrossRefGoogle Scholar
Müller, R., Abaid, N., Boreyko, J. B., Fowlkes, C., Goel, A. K., Grimm, C., Jung, S., Kennedy, B., Murphy, C. & Cushing, N. D. 2018. Biodiversifying Bioinspiration. Bioinspiration & Biomimetics, Vol. 13, p. 053001.CrossRefGoogle Scholar
Reimers, N. & Gurevych, I. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Association for Computational Linguistics. 2019 Hong Kong, China., pp. 3982–3992. https://doi.org/10.18653/v1/D19-1410CrossRefGoogle Scholar
Sarica, S., Luo, J. & Wood, K. L. 2020. Technet: Technology Semantic Network Based On Patent Data. Expert Systems With Applications, Vol. 142, p. 112995. https://doi.org/10.1016/j.eswa.2019.112995CrossRefGoogle Scholar
Shi, F., Chen, L., Han, J. & Childs, P. 2017. A Data-driven Text Mining And Semantic Network Analysis For Design Information Retrieval. Journal Of Mechanical Design, Vol. 139. https://doi.org/10.1115/1.4037649CrossRefGoogle Scholar
Shu, L.H., Ueda, K., Chiu, I., Cheong, H., 2011. Biologically Inspired Design. Cirp Ann. Vol. 60, pp. 673–693. https://doi.org/10.1016/j.cirp.2011.06.001CrossRefGoogle Scholar
Siddharth, L. & Chakrabarti, A. 2018. Evaluating The Impact Of Idea-inspire 4.0 On Analogical Transfer Of Concepts. Artificial Intelligence For Engineering Design, Analysis And Manufacturing, Vol. 32, pp. 431–448. https://doi.org/10.1017/s0890060418000136CrossRefGoogle Scholar
Zuo, H., Jing, Q., Song, T., Sun, L., Childs, P. & Chen, L. 2022. Wikilink: An Encyclopedia-based Semantic Network For Design Creativity. Journal Of Intelligence, Vol. 10, p. 103. https://doi.org/10.3390/jintelligence10040103CrossRefGoogle Scholar