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

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 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 in 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.

A Preprint Version

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Cited by in corpus (8)

• Deep Learning for Technical Document Classification
• AutoTRIZ: Automating Engineering Innovation with TRIZ and Large Language Models
• Inkspire: Supporting Design Exploration with Generative AI through Analogical Sketching
• Patent Data for Engineering Design: A Critical Review and Future Directions
• Technology Fitness Landscape for Design Innovation: A Deep Neural Embedding Approach Based on Patent Data
• BioSpark: Beyond Analogical Inspiration to LLM-augmented Transfer
• Engineering Knowledge Graph from Patent Database
• Development and Evaluation of a Retrieval-Augmented Generation Tool for Creating SAPPhIRE Models of Artificial Systems