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Data-Driven Design-By-Analogy: State-of-the-Art and Future Directions

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Jiang, Shuo, Hu, Jie, Wood, Kristin L., Luo, Jianxi·2022·Journal of Mechanical Design
DOI 10.1115/1.4051681
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Abstract

Design-by-analogy (DbA) is a design methodology wherein new solutions, opportunities, or designs are generated in a target domain based on inspiration drawn from a source domain; it can benefit designers in mitigating design fixation and improving design ideation outcomes. Recently, the increasingly available design databases and rapidly advancing data science and artificial intelligence (AI) technologies have presented new opportunities for developing data-driven methods and tools for DbA support. In this study, we survey existing data-driven DbA studies and categorize individual studies according to the data, methods, and applications into four categories, namely, analogy encoding, retrieval, mapping, and evaluation. Based on both nuanced organic review and structured analysis, this paper elucidates the state-of-the-art of data-driven DbA research to date and benchmarks it with the frontier of data science and AI research to identify promising research opportunities and directions for the field. Finally, we propose a future conceptual data-driven DbA system that integrates all propositions.

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  • In research settings, generative AI techniques can hypothesize promising new directions by creatively combining concepts.

    “In research settings, these generative AI techniques can hypothesize promising new directions by creatively combining concepts [12].”

    From Generative AI in medical practice: in-depth exploration of privacy and security challenges (2024) · cites this paper as [12]

    The analogy-based design synthesis section (p. 13) proposes using generative models to create new designs guided by analogies. The Applications section (p. 9) also describes tools that combine ideas for design ideation. Neither passage says these techniques hypothesize promising new research directions; the paper's application is engineering design. (LLM, conf=0.86)

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Cited by

Sorted by citations

Authors
Jiang, S., Hu, J., Wood, K. L., & Luo, J.
Affiliations
  • Shanghai Jiao Tong University (CN)
  • University of Colorado Denver (US)
  • Singapore University of Technology and Design (SG)
ORCID
Venue
Journal of Mechanical Design
Publisher
ASME International
Published
2021-09-09
Volume
144
Issue
2
ISSN
  • 0161-8458 (ISSN-L)
  • 1050-0472
  • 1528-9001
DOI
10.1115/1.4051681
OpenAlex
W3183442239
Cited by
86
References
6
Citations by year
2026: 9 · 2025: 20 · 2024: 21 · 2023: 21 · 2022: 14 · 2021: 1
FWCI
6.08
Type
journal article
Language
en
Open access
Licence
www.asme.org/publications-submissions/publishing-information/legal-policies (vor)
Locations
  • Journal of Mechanical Design (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
National Natural Science Foundation of China (52035007, 51975360)
Topics
  • Physical Sciences › Engineering › Mechanical Engineering › Design Education and Practice
  • Physical Sciences › Engineering › Computational Mechanics › Extenics and Innovation Methods
  • Physical Sciences › Engineering › Building and Construction › Sustainable Design and Development
Keywords
design by analogy, data-driven design, design ideation, design fixation, design database

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