In this section
@inproceedings{Chen:2023:10.1017/pds.2023.24,
author = {Chen, L and Cai, Z and Jiang, Z and Long, Q and Sun, L and Childs, P and Zuo, H},
doi = {10.1017/pds.2023.24},
pages = {231--240},
publisher = {Cambridge University Press (CUP)},
title = {A knowledge-based ideation approach for bio-inspired design},
url = {http://dx.doi.org/10.1017/pds.2023.24},
year = {2023}
}
TY - CPAPER
AB - 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.
AU - Chen,L
AU - Cai,Z
AU - Jiang,Z
AU - Long,Q
AU - Sun,L
AU - Childs,P
AU - Zuo,H
DO - 10.1017/pds.2023.24
EP - 240
PB - Cambridge University Press (CUP)
PY - 2023///
SP - 231
TI - A knowledge-based ideation approach for bio-inspired design
UR - http://dx.doi.org/10.1017/pds.2023.24
UR - http://hdl.handle.net/10044/1/106042
ER -
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