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Function-based, biologically inspired concept generation

Published online by Cambridge University Press:  25 October 2010

Jacquelyn K.S. Nagel ,
Robert L. Nagel ,
Robert B. Stone  and
Daniel A. McAdams
Jacquelyn K.S. Nagel
Affiliation:
Design Engineering Lab, Oregon State University, Corvallis, Oregon, USA
Robert L. Nagel
Affiliation:
School of Engineering, James Madison University, Harrisonburg, Virginia, USA
Robert B. Stone
Affiliation:
Design Engineering Lab, Oregon State University, Corvallis, Oregon, USA
Daniel A. McAdams
Affiliation:
Department of Mechanical Engineering, Texas A&M University, College Station, Texas, USA
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Abstract

The natural world provides numerous cases for inspiration in engineering design. Biological organisms, phenomena, and strategies, which we refer to as biological systems, provide a rich set of analogies. These systems provide insight into sustainable and adaptable design and offer engineers billions of years of valuable experience, which can be used to inspire engineering innovation. This research presents a general method for functionally representing biological systems through systematic design techniques, leading to the conceptualization of biologically inspired engineering designs. Functional representation and abstraction techniques are used to translate biological systems into an engineering context. The goal is to make the biological information accessible to engineering designers who possess varying levels of biological knowledge but have a common understanding of engineering design. Creative or novel engineering designs may then be discovered through connections made between biology and engineering. To assist with making connections between the two domains concept generation techniques that use biological information, engineering knowledge, and automatic concept generation software are employed. Two concept generation approaches are presented that use a biological model to discover corresponding engineering components that mimic the biological system and use a repository of engineering and biological information to discover which biological components inspire functional solutions to fulfill engineering requirements. Discussion includes general guidelines for modeling biological systems at varying levels of fidelity, advantages, limitations, and applications of this research. The modeling methodology and the first approach for concept generation are illustrated by a continuous example of lichen.

Keywords

Biologically InspiredConcept GenerationEngineering DesignFunction-Based Design
Type
Special Issue Articles
Information
AI EDAM , Volume 24 , Issue 4: Biologically Inspired Design , November 2010 , pp. 521 - 535
Copyright
Copyright © Cambridge University Press 2010

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References

REFERENCES

Agrawal, S.K. (2007). Sparrow flapping wing micro air vehicle. Accessed at http://mechsys4.me.udel.edu/research/birdproject/Google Scholar
Ahmadjian, V. (1993). The Lichen Symbiosis. Waltham, MA: Blaisdell Publishing.Google Scholar
Balazs, M.E., & Brown, D.C. (2001). Design simplification by analogical reasoning. In Knowledge Intensive Computer Aided Design (Rizzi, C., Cugini, U., & Wozny, M.J., Eds.). Dordrecht: Kluwer Academic.Google Scholar
Bar-Cohen, Y. (2006 a). Biomimetics—using nature to inspire human innovation. Journal of Bioinspiration and Biomimetics 1(1), 1–12.CrossRefGoogle ScholarPubMed
Bar-Cohen, Y. (2006 b). Biomimetics Biologically Inspired Technologies. Boca Raton, FL: CRC/Taylor & Francis.Google Scholar
Bhatta, S.R., & Goel, A.K. (1997). An analogical theory of creativity in design. Proc. 2nd Int. Conf. Case Based Reasoning Research and Development, pp. 565–574, LNCS Vol. 1266. Berlin: Springer.CrossRefGoogle Scholar
Birnbaum, L., Colling, G., Brand, M., Freed, M., Krulwich, B., & Pryor, L. (1991). A model-based approach to the construction of adaptive case-based planning systems. Proc. Case-Based Reasoning Workshop (Bareiss, R., Ed.), pp. 391–397. San Mateo, CA: Morgan Kaufmann.Google Scholar
Bohm, M., Vucovich, J., & Stone, R. (2008). Using a design repository to drive concept generation. Journal of Computer and Information Science in Engineering 8(1), 14502.CrossRefGoogle Scholar
Brebbia, C.A. (Ed.). (2006). Proc. 3rd Int. Conf., Design and Nature III: Comparing Design in Nature With Science and Engineering. Southampton: Wessex Institute of Technology.Google Scholar
Brebbia, C.A., & Collins, M.W. (Eds). (2004). Proc. 2nd Int. Conf., Design and Nature II: Comparing Design in Nature With Science and Engineering. Southampton: Wessex Institute of Technology.Google Scholar
Brebbia, C.A., Sucharov, L.J., & Pascolo, P. (Eds.). (2002). Proc. 1st Int. Conf., Design and Nature I: Comparing Design in Nature With Science and Engineering. Southampton: Wessex Institute of Technology.Google Scholar
Brodo, I.M., Sharnoff, S.D., & Sharnoff, S. (2001). Lichens of North America. New Haven, CT: Yale University Press.Google Scholar
Bryant, C., McAdams, D., Stone, R., Kurtoglu, T., & Campbell, M. (2005). A computational technique for concept generation. Proc. ASME 2005 IDETC/CIE Conf., Long Beach, CA.Google Scholar
Bryant, C., Stone, R., McAdams, D., Kurtoglu, T., & Campbell, M. (2005). Concept generation from the functional basis of design. Proc. Int. Conf. Engineering Design, Melbourne, Australia.Google Scholar
Campbell, N.A., & Reece, J.B. (2003). Biology. San Francisco, CA: Pearson Benjamin Cummings.Google ScholarPubMed
Casakin, H. (2006). Assessing the use of metaphors in the design process. Environment and Planning B: Planning and Design 33(2), 253–268.CrossRefGoogle Scholar
Casakin, H. (2007). Metaphors in design problem solving: implications for creativity. International Journal of Design 1(2), 21–33.Google Scholar
Chakrabarti, A., Sarkar, P., Leelavathamma, B., & Nataraju, B.S. (2005). A functional representation for aiding biomimetic and artificial inspiration of new ideas. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 19, 113–132.CrossRefGoogle Scholar
Cheong, H., Shu, L.H., Stone, R.B., & McAdams, D.A. (2008). Translating terms of the functional basis into biologically meaningful words. Proc. ASME 2008 Design Engineering Technical Conf. Computers and Information in Engineering Conf., New York.Google Scholar
Chiu, I., & Shu, L.H. (2007 a). Biomimetic design through natural language analysis to facilitate cross-domain information retrieval. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21(1), 45–59.CrossRefGoogle Scholar
Chiu, I., & Shu, L.H. (2007 b). Using language as related stimuli for concept generation. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21(2), 103–121.CrossRefGoogle Scholar
Cross, N. (1996). Creativity in design: not leaping but bridging. In Creativity and Cognition (Candy, L., & Edmonds, E., Eds.), pp. 27–35. Loughborough: Loughborough University.Google Scholar
Cutherell, D. (1996). Product architecture. In The PDMA Handbook of New Product Development (Rosenau, M. Jr., Ed.). New York: Wiley.Google Scholar
De Mantaras, R.L., & Plaza, E. (1997). Case-based reasoning: an overview. AI Communications 10(1), 21–29.Google Scholar
Dieter, G. (1991). Engineering Design: A Materials and Processing Approach. New York: McGraw–Hill.Google Scholar
Erden, M.S., Komoto, H., Beek, T.J.V., D'Amelio, V., Echavarria, E., & Tomiyama, T. (2008). A review of functional modeling: approaches and applications. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 22, 147–169.CrossRefGoogle Scholar
Forbes, P. (2006). The Gecko's Foot: Bio-Inspiration: Engineering New Materials From Nature. New York: W.W. Norton.Google Scholar
French, M.J. (1994). Invention and Evolution Design in Nature and Engineering. New York: Cambridge University Press.CrossRefGoogle Scholar
Gentner, D. (1983). Structure-mapping: a theoretical framework for analogy. Cognitive Science 7, 155–170.Google Scholar
Gentner, D. (1988). Analogical inference and access. In Analogica (Prieditis, A., Ed.), LNCS Vol. 230. Los Altos, CA: Morgan Kaufmann.Google Scholar
Gick, M., & Holyoak, K. (1980). Analogical problem-solving. Cognitive Psychology 12, 306–355.CrossRefGoogle Scholar
Goel, A. (1997). Design, analogy and creativity. IEEE Expert Intelligent Systems and Their Applications 12(3), 62–70.Google Scholar
Goel, A., & Chandrasekaran, B. (1988). Integrating model-based reasoning with case based reasoning for design problem solving. Proc. AAAI-88 Workshop on AI in Design, Minneapolis, MN.Google Scholar
Gordon, W.J.J. (1961). Synectics, the Development of Creative Capacity. New York: Harper.Google Scholar
Haas, J., Aaronson, J.S., & Overton, G.C. (1993). Analogical reasoning for knowledge discovery in a molecular biology database. Proc. 2nd Int. Conf. Information and Knowledge Management, pp. 554–564. New York: ACM.Google Scholar
Helms, M., Vattam, S.S., & Goel, A.K. (2009). Biologically inspired design: products and processes. Design Studies 30(5), 606–622.CrossRefGoogle Scholar
Henderson, I.F., & Lawrence, E. (2005). Henderson's Dictionary of Biology. Essex: Pearson Education.Google Scholar
Hey, J., Linsey, J., Agogino, A.M., & Wood, K.L. (2008). Analogies and metaphors in creative design. International Journal of Engineering Education 24(2), 283–294.Google Scholar
Hirtz, J., Stone, R., McAdams, D., Szykman, S., & Wood, K. (2002). A functional basis for engineering design: reconciling and evolving previous efforts. Research in Engineering Design 13(2), 65–82.CrossRefGoogle Scholar
Hofstadter, D.R. (1995). Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought. New York: Basic Books.Google Scholar
Kolodner, J.L., Simpson, R.L., & Sycara, K. (1985). A process model of case-based reasoning in problem solving. Proc. 9th Int. Joint Conf. Artificial Intelligence, pp. 284–290.Google Scholar
Lindemann, U., & Gramann, J. (2004). Engineering design using biological principles. Int. Design Conference, DESIGN 2004, Dubrovnik.Google Scholar
Linsey, J., Wood, K., & Markman, A. (2008). Modality and representation in analogy. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 22(2), 85–100.CrossRefGoogle Scholar
Maher, M.L., & de Silva Garza, A.G. (1996). Developing case-based reasoning for structural design. IEEE Intelligent Systems 11(3), 42–52.Google Scholar
Mak, T.W., & Shu, L.H. (2004). Abstraction of biological analogies for design. CIRP Annals 531(1), 117–120.CrossRefGoogle Scholar
Mak, T.W., & Shu, L.H. (2008). Using descriptions of biological phenomena for idea generation. Research in Engineering Design 19(1), 21–28.CrossRefGoogle Scholar
Matrin, E., & Hine, R.S. (2000). Oxford Dictionary of Biology. Oxford: Oxford University Press.Google Scholar
McAdams, D., & Wood, K. (2000). Quantitative measures for design by analogy. Proc. ASME 2000 Design Engineering Technical Conf. Computers and Information in Engineering Conf., Paper No. DETC2000/DTM-14562. New York: ASME.Google Scholar
Miles, L. (1961). Techniques of Value Analysis and Engineering. New York: McGraw–Hill.Google Scholar
Nagel, J.K.S., Stone, R.B., & McAdams, D.A. (2010). An engineering-to-biology thesaurus for engineering design. Proc. ASME 2010 IDETC/CIE Conf., Montreal.Google Scholar
Nagel, R., Perry, K., Stone, R., & McAdams, D. (2009). FunctionCAD: an open source functional modeling application based on the function design framework. Proc. ASME 2009 IDETC/CIE Conf., San Diego, CA.Google Scholar
Nagel, R., Tinsley, A., Midha, P., McAdams, D., Stone, R., & Shu, L. (2008). Exploring the use of functional models in biomimetic design. Journal of Mechanical Design 130(12), 11–23.CrossRefGoogle Scholar
Nash, T.H. (2008). Lichen Biology. New York: Cambridge University Press.CrossRefGoogle Scholar
Otto, K.N., & Wood, K.L. (2001). Product Design: Techniques in Reverse Engineering and New Product Development. Upper Saddle River, NJ: Prentice–Hall.Google Scholar
Pahl, G., Beitz, W., Feldhusen, J., & Grote, K.H. (2007). Engineering Design: A Systematic Approach. Berlin: Springer–Verlag.CrossRefGoogle Scholar
Prince, G.M. (1967). The operational mechanism of synectics. Journal of Creative Behavior 2(1), 1–13.CrossRefGoogle Scholar
Prince, G.M. (1970). The Practice of Creativity. New York: Collier Books.Google Scholar
Raven, P.H., & Johnson, G.B. (2002). Biology. Boston: McGraw–Hill.Google Scholar
Sarkar, P., Phaneendra, S., & Chakrabarti, A. (2008). Developing engineering products using inspiration from nature. Journal of Computing and Information Science in Engineering 8(3), 1–9.CrossRefGoogle Scholar
Shu, L.H., Stone, R.B., McAdams, D.A., & Greer, J.L. (2007). Integrating function-based and biomimetic design for automatic concept generation. Proc. Int. Conf. Engineering Design, Paris.Google Scholar
Slade, S. (1991). Case-based reasoning: a reseach paradigm. AI Magazine 12(1), 42–55.Google Scholar
Srinivasan, V., & Chakrabarti, A. (2009 a). SAPPhIRE—an approach to analysis and synthesis. Proc. 3rd Symp. Research in Product Design, Centre for Product Design and Manufacturing, Indian Institute of Science, Bangalore, India.Google Scholar
Srinivasan, V., & Chakrabarti, A. (2009 b). SAPPhIRE—an approach to analysis and synthesis. Proc. Int. Conf. Engineering Design, Stanford, CA.Google Scholar
Stone, R., & Wood, K. (2000). Development of a functional basis for Design. Journal of Mechanical Design 122(4), 359–370.CrossRefGoogle Scholar
Stroble, J.K., Stone, R.B., McAdams, D.A., Goeke, M.S., & Watkins, S.E. (2009). Automated retrieval of non-engineering domain solutions to engineering problems. Proc. CIRP Design Conf. 2009, pp. 78–85.Google Scholar
Stroble, J.K., Watkins, S.E., Stone, R.B., McAdams, D.A., & Shu, L.H. (2008). Modeling the cellular level of natural sensing with the functional basis for the design of biomimetic sensor technology. Proc. IEEE Region 5 Technical Conf., Kansas City, MO.Google Scholar
Thompson, G., & Lordan, M. (1999). A review of creativity principles applied to engineering design. Journal of Process Mechanical Engineering 213(1), 17–31.CrossRefGoogle Scholar
Tsujimoto, K., Miura, S., Tsumaya, A., Nagai, Y., Chakrabarti, A., & Taura, T. (2008). A method for creative behavioral design based on analogy and blending from natural things. Proc. ASME 2008 IDETC/CIE, Paper No. DETC2008/DTM-49389. New York: ASME.Google Scholar
Ullman, D.G. (2009). The Mechanical Design Process, 4th ed.New York: McGraw–Hill.Google Scholar
Ulrich, K.T., & Eppinger, S.D. (2004). Product Design and Development. Boston: McGraw–Hill/Irwin.Google Scholar
Vakili, V., & Shu, L.H. (2007). Including functional models of biological phenomena as design stimuli. Proc. ASME 2007 IDETC/CIE, Las Vegas, NV.Google Scholar
Van der Spiegel, J., & Nishimura, M. (2003). Biologically inspired vision sensor for the detection of higher-level image features. Proc. IEEE Conf. Electron Devices and Solid-State Circuits.Google Scholar
Vattam, S., Helms, M., & Goel, A. (2008). Compound analogical design: interaction between problem decomposition and analogical transfer in biologically inspired design. Proc. 3rd Int. Conf. Design Computing and Cognition, pp. 377–396. Berlin: Springer.CrossRefGoogle Scholar
Vincent, J.F.V. (2004). Design in nature. In Optimisation Mechanics in Nature (Collins, M.W., Hunt, D.G., & Atherton, M.A., Eds.). Southampton: Wessex Institute of Technology.Google Scholar
Vincent, J.F.V., & Mann, D.L. (2002). Systematic technology transfer from biology to engineering. Philosophical Transactions of the Royal Society London A 360, 159–173.CrossRefGoogle ScholarPubMed
Voland, G. (2004). Engineering by Design. Upper Saddle River, NJ: Pearson Prentice–Hall.Google Scholar
Wen, H.-I., Zhang, S.-J., Hapeshi, K., & Wang, X.-F. (2008). An innovative methodology of product design from nature. Journal of Bionic Engineering 5(1), 75–84.CrossRefGoogle Scholar
White, R.J., Peng, G.C.Y., & Demir, S.S. (2009). Multiscale modeling of biomedical, biological, and behavioral systems (Part 1). IEEE EMBS Magazine 28(2), 12–13.Google ScholarPubMed
Wilson, J.O., & Rosen, D. (2007). Systematic reverse engineering of biological systems. Proc. ASME 2007 Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Las Vegas, NV.Google Scholar