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Beyond analogy: A model of bioinspiration for creative design

Abstract : Is biologically inspired design only an analogical transfer from biology to engineering? Actually, nature does not always bring “hands-on” solutions that can be analogically applied in classic engineering. Then, what are the different operations that are involved in the bioinspiration process and what are the conditions allowing this process to produce a bioinspired design? In this paper, we model the whole design process in which bioinspiration is only one element. To build this model, we use a general design theory, concept–knowledge theory, because it allows one to capture analogy as well as all other knowledge changes that lead to the design of a bioinspired solution.We ground this model on well-described examples of biologically inspired designs available in the scientific literature. These examples include Flectofinw, a hingeless flapping mechanism conceived for fac¸ade shading, and WhalePower technology, the introduction of bumps on the leading edge of airfoils to improve aerodynamic properties. Our modeling disentangles the analogical aspects of the biologically inspired design process, and highlights the expansions occurring in both knowledge bases, scientific (nonbiological) and biological, as well as the impact of these expansions in the generation of new concepts (concept partitioning). This model also shows that bioinspired design requires a special form of collaboration between engineers and biologists. Contrasting with the classic oneway transfer between biology and engineering that is assumed in the literature, the concept–knowledge framework shows that these collaborations must be “mutually inspirational” because both biological and engineering knowledge expansions are needed to reach a novel solution.
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Submitted on : Tuesday, December 6, 2016 - 9:07:16 AM
Last modification on : Wednesday, November 17, 2021 - 12:31:24 PM
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Armand Hatchuel, Camila Freitas Salgueiredo. Beyond analogy: A model of bioinspiration for creative design. AI EDAM, Cambridge University Press (CUP), 2016, 30, pp.159 - 170. ⟨10.1017/S0890060416000044⟩. ⟨hal-01396212⟩



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