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Function-based, biologically inspired concept generation

Published online by Cambridge University Press:  25 October 2010

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


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.

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