Hostname: page-component-7c8c6479df-ph5wq Total loading time: 0 Render date: 2024-03-27T15:41:55.028Z Has data issue: false hasContentIssue false

A computational approach to biologically inspired design

Published online by Cambridge University Press:  20 April 2012

Jacquelyn K.S. Nagel*
Affiliation:
School of Engineering, James Madison University, Harrisonburg, Virginia, USA
Robert B. Stone
Affiliation:
Design Engineering Lab, Department of Mechanical, Industrial and Manufacturing Engineering, Oregon State University, Corvallis, Oregon, USA
*
Reprint requests to: Jacquelyn K.S. Nagel, School of Engineering, James Madison University, 801 Carrier Drive, MSC 4113, Harrisonburg, VA 22807, USA. E-mail: nageljk@jmu.edu

Abstract

The natural world provides numerous cases for analogy and inspiration in engineering design. During the early stages of design, particularly during concept generation when several variants are created, biological systems can be used to inspire innovative solutions to a design problem. However, identifying and presenting the valuable knowledge from the biological domain to an engineering designer during concept generation is currently a somewhat disorganized process or requires extensive knowledge of the biological system. To circumvent the knowledge requirement problem, we developed a computational approach for discovering biological inspiration during the early stages of design that integrates with established function-based design methods. This research defines and formalizes the information identification and knowledge transfer processes that enable systematic development of biologically inspired designs. The framework that supports our computational design approach is provided along with an example of a smart flooring device to demonstrate the approach. Biologically inspired conceptual designs are presented and validated through a literature search and comparison to existing products.

Type
Special Issue Articles
Copyright
Copyright © Cambridge University Press 2012

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

Addlesee, M.D., Jones, A., Livesey, F., & Samaria, F. (1997). The ORL active floor. IEEE Personal Communication 4(5), 3541.Google Scholar
Bar-Cohen, Y. (2006 a). Biomimetics—Using nature to inspire human innovation. Journal of Bioinspiration and Biomimetics 1, P1P12.CrossRefGoogle ScholarPubMed
Bar-Cohen, Y. (2006 b). Biomimetics Biologically Inspired Technologies. Boca Raton, FL: CRC Press/Taylor & Francis.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), 1450214508.Google Scholar
Bouchard, C., Omhover, J.-F., Mougenot, C., Aoussat, A., & Westerman, S.J. (2008). TRENDS: a content-based information retrieval system for designers. Proc. Design Computing and Cognition '08, Vol. 4, pp. 593611. Atlanta, GA: Springer Science + Business Media B.V.CrossRefGoogle Scholar
Brebbia, C.A. (2006). Design and Nature III: Comparing Design in Nature With Science and Engineering. Southampton: WIT.Google Scholar
Brebbia, C.A. (2008). Design & Nature IV: Comparing Design in Nature With Science and Engineering. Southampton: WIT.Google Scholar
Brebbia, C.A., & Carpi, A. (2010). Design & Nature V: Comparing Design in Nature With Science and Engineering. Southampton: WIT.Google Scholar
Brebbia, C.A., & Collins, M.W. (2004). Design and Nature II: Comparing Design in Nature With Science and Engineering. Southampton: WIT.Google Scholar
Brebbia, C.A., Sucharov, L.J., & Pascolo, P. (2002). Design and Nature: Comparing Design in Nature With Science and Engineering. Southampton: WIT.Google Scholar
Bryant, C., Bohm, M., McAdams, D., & Stone, R. (2007). An interactive morphological matrix computational design tool: a hybrid of two methods. Proc. ASME 2007 IDETC/CIE, Las Vegas, NV.Google Scholar
Bryant, C., McAdams, D., Stone, R., Kurtoglu, T., & Campbell, M. (2005). A computational technique for concept generation. Proc. 2005 ASME IDETC/CIE, 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
Bryant Arnold, C.R., Stone, R.B., & McAdams, D.A. (2008). MEMIC: an interactive morphological matrix tool for automated concept generation. Proc. Industrial Engineering Research Conf.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(2), 113132.CrossRefGoogle Scholar
Cheong, H., Shu, L.H., Stone, R.B., & McAdams, D.A. (2008). Translating terms of the functional basis into biologically meaningful words. 2008 Proc. ASME IDETC/CIE, 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), 4559.Google 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), 103121.CrossRefGoogle Scholar
Crane, D. (2005). New high-tech sensor-laden smart carpet may revolutionize building security. Defense Review. Accessed at http://www.defensereview.com/new-high-tech-sensor-laiden-smart-carpet-may-revolutionize-building-security/Google Scholar
Cross, N. (2008). Engineering Design Methods: Strategies for Product Design. Chichester: Wiley.Google Scholar
Dym, C.L., & Little, P. (2004). Engineering Design: A Project-Based Introduction. New York: Wiley.Google Scholar
Gordon, W.J.J. (1961). Synectics, the Development of Creative Capacity. New York: Harper.Google Scholar
Helms, M., Vattam, S.S., & Goel, A.K. (2009). Biologically inspired design: products and processes. Design Studies 30(5), 606622.CrossRefGoogle 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), 6582.CrossRefGoogle Scholar
Hong-Zhong, H., Bo, R., & Chen, W. (2006). An integrated computational intelligence approach to product concept generation and evaluation. Mechanism and Machine Theory 41(5), 567583.Google Scholar
Hyman, B. (1998). Engineering Design. Englewood Cliffs, NJ: Prentice–Hall.Google Scholar
Institution of Electrical Engineers. (2003). Research news—walk this way for the smart floor. Electronics Systems and Software 1(3), 57.Google Scholar
Jin, Y., & Li, W. (2007). Design concept generation: a hierarchical coevolutionary approach. Journal of Mechanical Design 129(10), 10121022.CrossRefGoogle Scholar
Kurfman, M., Stone, R., Rajan, J., & Wood, K. (2003). Experimental studies assessing the repeatability of a functional modeling derivation method. Journal of Mechanical Design 125(4), 682693.CrossRefGoogle Scholar
Kurtoglu, T., Campbell, M.I., Bryant, C.R., Stone, R.B., & McAdams, D.A. (2009). A component taxonomy as a framework for computational design synthesis. Journal of Computing and Information Science in Engineering 9(1), 011007.CrossRefGoogle Scholar
Kurtoglu, T., Swantner, A., & Campbell, M.I. (2008). Automating the conceptual design process: from black-box to component selection. Proc. Design Computing and Cognition '08, Vol. 7, pp. 553572. Atlanta, GA: Springer Science + Business Media B.V.CrossRefGoogle Scholar
Liau, W.-H., Wu, C.-L., & Fu, L.-C. (2008). Inhabitants tracking system in a cluttered home environment via floor load sensors. IEEE Transactions on Automation Science and Engineering 5(1), 1020.CrossRefGoogle Scholar
Lindemann, U., & Gramann, J. (2004). Engineering design using biological principles. Proc. Int. Design Conf., Design '04, 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), 85100.CrossRefGoogle Scholar
Lopez-Huertas, M.J. (1997). Thesarus structure design: a conceptual approach for improved interaction. Journal of Documentation 53(2), 139177.Google Scholar
Mak, T.W., & Shu, L.H. (2008). Using descriptions of biological phenomena for idea generation. Research in Engineering Design 19(1), 2128.Google Scholar
Nagel, J.K.S., Stone, R.B., & McAdams, D.A. (2010). An engineering-to-biology thesaurus for engineering design. Proc. 2010 ASME IDETC/CIE, Montreal.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), 1123.CrossRefGoogle Scholar
Nagel, R.L., Stone, R., & McAdams, D. (2007). A theory for the development of conceptual functional models for automation of manual processes. Proc. 2007 ASME IDETC/CIE, Las Vegas, NV.Google Scholar
Orr, R.J., & Abowd, G.D. (2000). The smart floor: a mechanism for natural user identification and tracking. Proc. Conf. Human Factors in Computing Systems (CHI), The Hague.Google 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.Google Scholar
Prince, G.M. (1967). The operational mechanism of synectics. Journal of Creative Behavior 2(1), 113.CrossRefGoogle Scholar
Prince, G.M. (1970). The Practice of Creativity. New York: Collier Books.Google Scholar
Purves, W.K., Sadava, D., Orians, G.H., & Heller, H.C. (2001). Life, The Science of Biology. Sunderland, MA: Sinauer Associates.Google Scholar
Richardson, B., Leydon, K., Fernström, M., & Paradiso, J.A. (2004). Z-Tiles: building blocks for modular, pressure-sensing floorspaces. Proc. Conf. Human Factors in Computing Systems (CHI), Vienna.Google Scholar
Shu, L.H., Hansen, H.N., Gegeckaite, A., Moon, J., & Chan, C. (2006). Case study in biomimetic design: handling and assembly of microparts. Proc. ASME 2006 IDETC/CIE, Philadelphia, PA.Google Scholar
Srinivasan, V., & Chakrabarti, A. (2009). 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), 359370.Google Scholar
Stroble, J.K., Stone, R.B., McAdams, D.A., & Watkins, S.E. (2009). An engineering-to-biology thesaurus to promote better collaboration, creativity and discovery. Proc. CIRP Design Conf. 2009, pp. 353368, Cranfield.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
Vincent, J.F.V., Bogatyreva, O.A., Bogatyrev, N.R., Bowyer, A., & Pahl, A.-K. (2006). Biomimetics: its practice and theory. Journal of the Royal Society Interface 3, 471482.Google Scholar
Voland, G. (2004). Engineering by Design. Upper Saddle River, NJ: Pearson Prentice Hall.Google Scholar
Vorwerk & Co. (2004). Infineon Thinking Carpet. Wuppertal, Germany: Vorwerk.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), 7584.Google Scholar
Wilson, J., Chang, P., Yim, S., & Rosen, D. (2009). Developing a bio-inspired design repository using ontologies. Proc. 2009 ASME IDETC/CIE.Google Scholar
Wood, W.H., Yang, M.C., Cutkosky, M.R., & Agogino, A.M. (1998). Design information retrieval: improving access to the informal side of design. Proc. ASME 1998 IDETC/CIE, Atlanta, GA.Google Scholar
Yao, Zu, Xiao, R., & Zhang, X. (2009). Automated conceptual design of mechanisms using enumeration and functional reasoning. International Journal of Materials and Product Technology 34(3), 273294.Google Scholar