Hostname: page-component-7479d7b7d-8zxtt Total loading time: 0 Render date: 2024-07-10T22:04:20.388Z Has data issue: false hasContentIssue false
  • English
  • Français

Beyond analogy: A model of bioinspiration for creative design

Published online by Cambridge University Press:  18 April 2016

Camila Freitas Salgueiredo  and
Armand Hatchuel
Camila Freitas Salgueiredo*
Affiliation:
Renault, Technocentre Guyancourt, Guyancourt, France LIVIC-COSYS, IFSTTAR, Versailles, France Sorbonne Universités, Université Pierre et Marie Curie Paris, Paris, France
Armand Hatchuel
Affiliation:
MinesParisTech–PSL Research University, CGS Center for Management Science, Paris, France
*
Reprint requests to: Camila Freitas Salgueiredo, LIVIC-COSYS IFSTTAR, 25 allée des Marronniers, Versailles F-78000, France. E-mail: camila.freitassalgueiredo@ens.univ-evry.fr
Get access Rights & Permissions [Opens in a new window]

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 Flectofin®, a hingeless flapping mechanism conceived for façade 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 one-way 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.

Keywords

Biologically Inspired DesignConcept–Knowledge Design TheoryInnovation
Type
Special Issue Articles
Information
AI EDAM , Volume 30 , Issue 2: Design Computing and Cognition (DCC'14) , May 2016 , pp. 159 - 170
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Agogué, M., Kazakçi, A., Hatchuel, A., Le Masson, P., Weil, B., Poirel, N., & Cassotti, M. (2014). The impact of type of examples on originality: explaining fixation and stimulation effects. Journal of Creative Behavior 48(1), 1–12.CrossRefGoogle Scholar
Autumn, K., Liang, Y.A., Hsieh, S.T., Zesch, W., Chan, W.P., Kenny, T.W., Fearing, R., & Full, R.J. (2000). Adhesive force of a single gecko foot-hair. Nature 405(6787), 681–684.Google Scholar
Badarnah, L., & Kadri, U. (2014). A methodology for the generation of biomimetic design concepts. Architectural Science Review. Advance online publication.Google Scholar
Bar-Cohen, Y. (2012). Biomimetics: Nature Based Innovations. Boca Raton, FL: CRC Press.Google Scholar
Barthlott, W., & Neinhuis, C. (1997). Purity of the sacred lotus, or escape from contamination in biological surfaces. Planta 202(1), 1–8.Google Scholar
Bartlett, M.D., Croll, A.B., King, D.R., Paret, B.M., Irschick, D.J., & Crosby, A.J. (2012). Looking beyond fibrillar features to scale gecko-like adhesion. Advanced Materials 24(8), 1078–1083.Google Scholar
Baumgartner, A., Harzheim, L., & Mattheck, C. (1992). {SKO} (Soft Kill Option): the biological way to find an optimum structure topology. International Journal of Fatigue 14(6), 387– 393.CrossRefGoogle Scholar
Benyus, J.M. (1997). Biomimicry: Innovation Inspired by Nature. New York: William Morrow.Google Scholar
Bhushan, B. (2009). Biomimetics: lessons from nature—an overview. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367(1893), 1445–1486.Google Scholar
Biomimicry 3.8. (2014). Biomimicry design lens: Biomimicry thinking. Accessed at http://-biomimicry.net/about/biomimicry/biomimicry-designlens/biomimicry-thinking/ on October 14, 2014.Google Scholar
Bonser, R.H. (2006). Patented biologically-inspired technological innovations: a twenty year view. Journal of Bionic Engineering 3(1), 39–41.Google Scholar
Bushnell, D. M., & Moore, K.J. (1991). Drag reduction in nature. Annual Review of Fluid Mechanics 23(1), 65–79.CrossRefGoogle Scholar
Cheong, H., & Shu, L. (2013). Using templates and mapping strategies to support analogical transfer in biomimetic design. Design Studies 34(6), 706–728.Google Scholar
Chiu, I., & Shu, L. (2007). Biomimetic design through natural language analysis to facilitate cross-domain information retrieval. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21(1), 45–59.Google Scholar
Dawson, C., Vincent, J.F.V., & Rocca, A.-M. (1997). How pine cones open. Nature 390(6661), 668.Google Scholar
Deldin, J.-M., & Schuknecht, M. (2014). The AskNature database: enabling solutions in biomimetic design. In Biologically Inspired Design: Computational Methods and Tools (Goel, A.K., Ed.), pp. 17–27. London: Springer–Verlag.CrossRefGoogle Scholar
Fish, F.E., & Battle, J.M. (1995). Hydrodynamic design of the humpback whale flipper. Journal of Morphology 225(1), 51–60.Google Scholar
Fish, F.E., Weber, P.W., Murray, M.M., & Howle, L.E. (2011). The tubercles on humpback whales’ flippers: application of bio-inspired technology. Integrative and Comparative Biology 51(1), 203–213.CrossRefGoogle ScholarPubMed
Freitas Salgueiredo, C., & Hatchuel, A. (2014). Modeling biologically inspired design with the c-k theory. Proc. Design 2014 Conf., Dubrovnik, Croatia, May 19–22.Google Scholar
Goel, A., Zhang, G., Wiltgen, B., Zhang, Y., Vattam, S., & Yen, J. (2015). The design study library: collecting, analyzing and using case studies of biologically inspired design. Proc. Design Computing and Cognition'14 (Gero, J.S., Ed.), Vol. 14, London, July 19–24.Google Scholar
Goel, A.K., Vattam, S., Wiltgen, B., & Helms, M. (2014). Information processing theories of biologically inspired design. In Biologically Inspired Design: Computational Methods and Tools (Goel, A.K., Ed.), pp. 127–152. London: Springer–Verlag.Google Scholar
Hatchuel, A., Le Masson, P., Reich, Y., & Weil, B. (2011). A systematic approach of design theories using generativeness and robustness. Proc. 18th Int. Conf. Engineering Design (ICED 11), Impacting Society Through Engineering Design (Culley, S., Hicks, B., McAloone, T., Howard, T., & Reich, Y., Eds.), Vol. 2, pp. 87–97. Copenhagen: ICED.Google Scholar
Hatchuel, A., Le Masson, P., & Weil, B. (2011). Teaching innovative design reasoning: how concept–knowledge theory can help overcome fixation effects. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 25(1), 77–92.Google Scholar
Hatchuel, A., & Weil, B. (2003). A new approach of innovative design: an introduction to C-K theory. Proc. 14th Int. Conf. Engineering Design (ICED'03), pp. 109–124. Stockholm: ICED.Google Scholar
Hatchuel, A., & Weil, B. (2009). C-k design theory: an advanced formulation. Research in Engineering Design 19(4), 181–192.Google Scholar
Hatchuel, A., Weil, B., & Masson, P. (2012). Towards an ontology of design: lessons from C-K design theory and forcing. Research in Engineering Design 24(2), 1–17.Google Scholar
Helfman Cohen, Y., Reich, Y., & Greenberg, S. (2014, Oct.). Biomimetics: structure–function patterns approach. Journal of Mechanical Design 136(11), 111108.Google Scholar
Helms, M., & Goel, A. (2012). Analogical problem evolution in biologically inspired design. Proc. Design Computing and Cognition ‘12 (Gero, J.S., Ed.), pp. 3–19. Amsterdam: Springer.Google Scholar
Helms, M., Vattam, S.S., & Goel, A.K. (2009). Biologically inspired design: process and products. Design Studies 30(5), 606–622.Google Scholar
Johari, H., Henoch, C.W., Custodio, D., & Levshin, A. (2007). Effects of leading-edge protuberances on airfoil performance. AIAA Journal 45(11), 2634–2642.Google Scholar
Knippers, J., & Speck, T. (2012). Design and construction principles in nature and architecture. Bioinspiration & Biomimetics 7(1), 015002.CrossRefGoogle ScholarPubMed
Kroll, E., Le Masson, P., & Weil, B. (2014). Steepest-first exploration with learning-based path evaluation: uncovering the design strategy of parameter analysis with C-K theory. Research in Engineering Design 25(4), 351–373.CrossRefGoogle Scholar
Kwak, M.K., Pang, C., Jeong, H.-E., Kim, H.-N., Yoon, H., Jung, H.-S., & Suh, K.-Y. (2011). Towards the next level of bioinspired dry adhesives: new designs and applications. Advanced Functional Materials 21(19), 3606–3616.Google Scholar
Lepora, N.F., Verschure, P., & Prescott, T.J. (2013). The state of the art in biomimetics. Bioinspiration & Biomimetics 8(1), 013001.Google Scholar
Lienhard, J., Poppinga, S., Schleicher, S., Masselter, T., Speck, T., & Knippers, J. (2009). Abstraction of plant movements for deployable structures in architecture. Proc. 6th Plant Biomechanics Conference, pp. 389–397, Cayenne, French Guyana, November 16–21.Google Scholar
Lienhard, J., Schleicher, S., Poppinga, S., Masselter, T., Milwich, M., Speck, T., & Knippers, J. (2011). Flectofin: a hingeless flapping mechanism inspired by nature. Bioinspiration & Biomimetics 6(4), 045001.Google Scholar
Mak, T., & Shu, L. (2008). Using descriptions of biological phenomena for idea generation. Research in Engineering Design 19(1), 21–28.Google Scholar
Masselter, T., Barthlott, W., Bertling, J., Cichy, F., Hermann, M., Knippers, J., Luchsinger, R., Mattheck, C., Milwich, M., & Neinhuis, C. (2012). Biomimetic products. In Biomimetics: Nature Based Innovations (Bar-Cohen, Y., Ed.), pp. 377–429. Boca Raton, FL: CRC Press.Google Scholar
Matini, M., & Knippers, J. (2008). Application of “abstract formal patterns” for translating natural principles into the design of new deployable structures in architecture. In WIT Transactions on Ecology and the Environment: Vol. 114. Design and Nature IV (Brebbia, C.A., Ed.), pp. 147–156. Southampton: WIT Press.Google Scholar
Miklosovic, D.S., Murray, M.M., Howle, L.E., & Fish, F.E. (2004). Leading-edge tubercles delay stall on humpback whale (Megaptera novaeangliae) flippers. Physics of Fluids 16(5), L39–L42.Google Scholar
Nagel, J., Stone, R.B., & McAdams, D. (2014). Function-based biologically-inspired design. In Biologically Inspired Design: Computational Methods and Tools (Goel, A.K., Ed.), pp. 95–125. London: Springer–Verlag.CrossRefGoogle Scholar
Nagel, J.K., Nagel, R.L., Stone, R.B., & McAdams, D.A. (2010). Function-based, biologically inspired concept generation. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 24(4), 521–535.Google Scholar
Nagel, J.K., & Stone, R.B. (2012). A computational approach to biologically inspired design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 26(2), 161–176.Google Scholar
Reich, Y., Hatchuel, A., Shai, O., & Subrahmanian, E. (2012). A theoretical analysis of creativity methods in engineering design: casting and improving asit within C-K theory. Journal of Engineering Design 23(2), 137–158.Google Scholar
Sartori, J., Pal, U., & Chakrabarti, A. (2010). A methodology for supporting “transfer” in biomimetic design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 24(4), 483–506.Google Scholar
Shai, O., Reich, Y., Hatchuel, A., & Subrahmanian, E. (2013). Creativity and scientific discovery with infused design and its analysis with C-K theory. Research in Engineering Design 24(2), 201–214.Google Scholar
Shu, L. (2010). A natural-language approach to biomimetic design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 24(4), 507–519.Google Scholar
Shu, L., Ueda, K., Chiu, I., & Cheong, H. (2011). Biologically inspired design. CIRP Annals of Manufacturing Technology 60(2), 673–693.Google Scholar
Singh, A.V., Rahman, A., Kumar, N.S., Aditi, A., Galluzzi, M., Bovio, S., Barozzi, S., Montani, E., & Parazzoli, D. (2012). Bio-inspired approaches to design smart fabrics. Materials & Design 36, 829–839.Google Scholar
Speck, T., & Speck, O. (2008). Process sequences in biomimetic research. In WIT Transactions on Ecology and the Environment: Vol. 114. Design and Nature IV (Brebbia, C.A., Ed.), pp. 3–11. Southampton: WIT Press.Google Scholar
Vandevenne, D., Caicedo, J., Verhaegen, P.-A., Dewulf, S., & Duflou, J. (2013). Webcrawling for a biological strategy corpus to support biologically-inspired design. Proc. CIRP Design 2012 (Chakrabarti, A., Ed.), pp. 83–92. London: Springer.Google Scholar
Vattam, S.S., Helms, M.E., & Goel, A.K. (2010). A content account of creative analogies in biologically inspired design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 24(4), 467–481.Google Scholar
Vincent, J.F. (2014). An ontology of biomimetics. In Biologically Inspired Design: Computational Methods and Tools (Goel, A.K., Ed.), pp. 269–285. London: Springer–Verlag.Google Scholar
Vincent, J.F., Bogatyreva, O.A., Bogatyrev, N.R., Bowyer, A., & Pahl, A.-K. (2006). Biomimetics: its practice and theory. Journal of the Royal Society Interface 3(9), 471–482.Google Scholar
Wilson, J.O., Rosen, D., Nelson, B.A., & Yen, J. (2010). The effects of biological examples in idea generation. Design Studies 31(2), 169–186.Google Scholar
Wiltgen, B., Goel, A., & Vattam, S. (2011). Representation, indexing, and retrieval of biological cases for biologically inspired design. In Case-Based Reasoning Research and Development (Ram, A., & Wiratunga, N., Eds.), LNCS, Vol. 6880, pp. 334–347. Berlin: Springer.Google Scholar