THE RELATIONSHIP BETWEEN FUNCTIONS AND OUTCOMES OF BIOLOGICALLY-INSPIRED DESIGN

Abstract Research in biologically-inspired design (BID) practice often focus on team composition or ideation based on an already discovered fascinating biological solution principle. However, how are the outcome of the early design phases affecting BID projects' quality? In this study, historical data from 91 reports from student teams documenting their BID efforts from a 3-week course constitute the data source. Thus, the relationship between design problem types, function types, functions descriptions and BID projects' quality is addressed. The study show that especially design problem types and function descriptions affect the BID projects' quality. For instance, BID projects dealing with open-ended problems yield better results than redesign problems with existing solutions operating in a very domain-limited solution space. Next, BID projects obtain the best results when using functions as drivers for analogy searching rather than properties. Finally, BID projects with certain function types seem to have more complicated conceptualization phases.

[1]  Ashok K. Goel,et al.  Biologically-Inspired Innovation in Engineering Design: a Cognitive Study , 2007 .

[2]  T. A. Lenau,et al.  Paradigms for biologically inspired design , 2018, Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[3]  Kathleen Richardson,et al.  ASEE Annual Conference and Exposition, Conference Proceedings , 2009 .

[4]  Brigitte Moench,et al.  Engineering Design A Systematic Approach , 2016 .

[5]  Torben Anker Lenau,et al.  ENGINEERING DESIGN OF AN ADAPTIVE LEG PROSTHESIS USING BIOLOGICAL PRINCIPLES , 2010 .

[6]  Mogens Myrup Andreasen,et al.  Conceptual Design: Interpretations, Mindset and Models , 2015 .

[7]  W. E. Eder,et al.  Theory of Technical Systems: A Total Concept Theory for Engineering Design , 1988 .

[8]  Pieter E. Vermaas,et al.  The coexistence of engineering meanings of function: Four responses and their methodological implications , 2013, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[9]  Lei Jiang,et al.  Multifunctional integration: from biological to bio-inspired materials. , 2011, ACS nano.

[10]  Behrouz Homayoun Far,et al.  Functional reasoning theories: Problems and perspectives , 2005, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[11]  Helena Hashemi Farzaneh,et al.  Bio-inspired design: the impact of collaboration between engineers and biologists on analogical transfer and ideation , 2020, Research in Engineering Design.

[12]  Sanjay P. Sane Bioinspiration and Biomimicry: What Can Engineers Learn from Biologists? , 2016 .

[13]  Kristin L. Wood,et al.  Development of a Functional Basis for Design , 2000 .

[14]  Pierre-Emmanuel Fayemi,et al.  What Do We Learn from Good Practices of Biologically Inspired Design in Innovation? , 2019, Applied Sciences.

[15]  John S. Gero,et al.  Design Prototypes: A Knowledge Representation Schema for Design , 1990, AI Mag..

[16]  Ashok K. Goel,et al.  The Four-Box Method: Problem Formulation and Analogy Evaluation in Biologically Inspired Design , 2014 .

[17]  Ashok K. Goel,et al.  Biologically inspired design: process and products , 2009 .

[18]  M. McHugh Interrater reliability: the kappa statistic , 2012, Biochemia medica.

[19]  Ethan Smith,et al.  Growing the practice of biomimicry: opportunities for mission-based organisations based on a global survey of practitioners , 2020, Technol. Anal. Strateg. Manag..