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Categorizing biological information based on function–morphology for bioinspired conceptual design

Published online by Cambridge University Press:  05 December 2016

Sooyeon Lee ,
Daniel A. McAdams  and
Elissa Morris
Sooyeon Lee
Affiliation:
Department of Mechanical Engineering, Texas A&M University, College Station, Texas, USA
Daniel A. McAdams*
Affiliation:
Department of Mechanical Engineering, Texas A&M University, College Station, Texas, USA
Elissa Morris
Affiliation:
Department of Mechanical Engineering, Texas A&M University, College Station, Texas, USA
*
Reprint requests to: Daniel A. McAdams, Texas A&M University, Mechanical Engineering Office Building, 3123 TAMU, College Station, TX 77843, USA. E-mail: [email protected]
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Abstract

A function-based keyword search is a concept generation methodology studied in the bioinspired design area that conveys textual biological inspiration for engineering design. Current keyword search methods are inefficient primarily due to the knowledge gap between engineering and biology domains. To improve current keyword search methods, we propose an algorithm that extracts and organizes morphology-based solutions from biological text. WordNet is utilized to discover morphological solutions in biological text. The novel algorithm also adapts latent semantic analysis and the expectation–maximization algorithm to categorize morphological solutions and group biological text. We introduce a novel penalty function that reflects the distance between functions (problems) and morphologies (solutions). The penalty function allows the algorithm to extract morphological solutions directly related to a design problem. We compare the output of the algorithm to manually extracted solutions for validation. A case study is included to exemplify the utility of the developed algorithm. Upon implementation of the algorithm, engineering designers can discover innovative solutions in biological text in a straightforward, efficient manner.

Keywords

Bioinspired DesignConceptual DesignKeyword SearchMorphologyText Mining
Type
Regular Articles
Information
AI EDAM , Volume 31 , Special Issue 3: Uncertainty Quantification for Engineering Design , August 2017 , pp. 359 - 375
Copyright
Copyright © Cambridge University Press 2016 

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