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Idea generation with Technology Semantic Network

Published online by Cambridge University Press:  03 March 2021

Serhad Sarica Open the ORCID record for Serhad Sarica [Opens in a new window] ,
Binyang Song ,
Jianxi Luo  and
Kristin L. Wood
Serhad Sarica*
Affiliation:
Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore
Binyang Song
Affiliation:
School of Engineering Design, Technology and Professional Programs, Pennsylvania State University, University Park, PA, USA
Jianxi Luo
Affiliation:
Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore SUTD-MIT International Design Centre, Singapore University of Technology and Design, Singapore
Kristin L. Wood
Affiliation:
Mechanical Engineering Department, College of Engineering, Design and Computing, University of Colorado Denver, Denver, CO, USA
*
Author for correspondence: Serhad Sarica, E-mail: serhad_sarica@mymail.sutd.edu.sg
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Abstract

There are growing efforts to mine public and common-sense semantic network databases for engineering design ideation stimuli. However, there is still a lack of design ideation aids based on semantic network databases that are specialized in engineering or technology-based knowledge. In this study, we present a new methodology of using the Technology Semantic Network (TechNet) to stimulate idea generation in engineering design. The core of the methodology is to guide the inference of new technical concepts in the white space surrounding a focal design domain according to their semantic distance in the large TechNet, for potential syntheses into new design ideas. We demonstrate the effectiveness in general, and use strategies and ideation outcome implications of the methodology via a case study of flying car design idea generation.

Keywords

Data-driven designflying caridea generationsemantic distanceTechnology Semantic Network
Type
Research Article
Information
AI EDAM , Volume 35 , Issue 3 , August 2021 , pp. 265 - 283
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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