Inherent in biologically inspired design (BID) is the selection of one or more analogs from which one or more strategies are extracted and transferred into the engineering domain. The selection of an analog is a fundamental step in biomimetic process, but locating relevant biological analogies can be challenging. Often, designers may fixate on an analogy or choose an established analogy without rigorous examination of alternatives. This practice is problematic—as basing a new design on an invalid assumption can lead to suboptimal results. This paper makes contribution to evaluation of analogy utility. The contribution is made by combining stochastic multicriteria acceptability analysis (SMAA) with a set of criteria, derived from BID, to assist multidisciplinary decision makers (DMs) in evaluating candidate design analogs. The resulting framework, which we call the biotransferability framework, is being developed to assist multidisciplinary teams to choose, rank, or sort candidate design analogs by assessing biology-to-engineering transfer risk.
Skip Nav Destination
Article navigation
November 2014
Research-Article
Using Stochastic Multicriteria Acceptability Analysis in Biologically Inspired Design as a Multidisciplinary Tool to Assess Biology-to-Engineering Transfer Risk for Candidate Analogs
M. Lindsey Williams,
M. Lindsey Williams
1
Mechanical Engineering,
e-mail: lindsey.williams@ttu.edu
Texas Tech University
,7th and Boston
,Lubbock, TX 79409
e-mail: lindsey.williams@ttu.edu
1Corresponding author.
Search for other works by this author on:
Atila Ertas,
Atila Ertas
Department of Mechanical Engineering,
e-mail: atila.ertas@ttu.edu
Texas Tech University
,7th and Boston
,Lubbock, TX 79409
e-mail: atila.ertas@ttu.edu
Search for other works by this author on:
Derrick Tate
Derrick Tate
Associate Professor
Founding Head
Department of Industrial Design,
e-mail: d.tate@ttu.edu
Founding Head
Department of Industrial Design,
XI'an Jiaotong-Liverpool University
,Suzhou, Jiangsu 215123
, China
e-mail: d.tate@ttu.edu
Search for other works by this author on:
M. Lindsey Williams
Mechanical Engineering,
e-mail: lindsey.williams@ttu.edu
Texas Tech University
,7th and Boston
,Lubbock, TX 79409
e-mail: lindsey.williams@ttu.edu
Atila Ertas
Department of Mechanical Engineering,
e-mail: atila.ertas@ttu.edu
Texas Tech University
,7th and Boston
,Lubbock, TX 79409
e-mail: atila.ertas@ttu.edu
Derrick Tate
Associate Professor
Founding Head
Department of Industrial Design,
e-mail: d.tate@ttu.edu
Founding Head
Department of Industrial Design,
XI'an Jiaotong-Liverpool University
,Suzhou, Jiangsu 215123
, China
e-mail: d.tate@ttu.edu
1Corresponding author.
Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received January 22, 2014; final manuscript received July 25, 2014; published online October 8, 2014. Assoc. Editor: Ashok K. Goel.
J. Mech. Des. Nov 2014, 136(11): 111107 (7 pages)
Published Online: October 8, 2014
Article history
Received:
January 22, 2014
Revision Received:
July 25, 2014
Citation
Lindsey Williams, M., Ertas, A., and Tate, D. (October 8, 2014). "Using Stochastic Multicriteria Acceptability Analysis in Biologically Inspired Design as a Multidisciplinary Tool to Assess Biology-to-Engineering Transfer Risk for Candidate Analogs." ASME. J. Mech. Des. November 2014; 136(11): 111107. https://doi.org/10.1115/1.4028170
Download citation file:
Get Email Alerts
Cited By
Related Articles
A Shared Framework of Reference, a First Step Toward Engineers’ and Biologists’ Synergic Reasoning in Biomimetic Design Teams
J. Mech. Des (April,2021)
Disruptions of Progress
Mechanical Engineering (November,2005)
Editorial
J. Mech. Des (July,2005)
Related Proceedings Papers
Related Chapters
A PSA Update to Reflect Procedural Changes (PSAM-0217)
Proceedings of the Eighth International Conference on Probabilistic Safety Assessment & Management (PSAM)
The New Breed of Knowledge Workers
Knowledge Tornado: Bridging the Corporate Knowledge Gap
mDFA Empirical Results
Modified Detrended Fluctuation Analysis (mDFA)