Agile conceptual design and validation based on multi-source product data and large language models: a review, framework, and outlook

Li, Shijiang, Zhou, Xingwei, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Chen, Jiancheng, Guo, Tao, Yang, Wanran and Hou, Liang 2025. Agile conceptual design and validation based on multi-source product data and large language models: a review, framework, and outlook. Journal of Engineering Design 36 (4) , pp. 473-503. 10.1080/09544828.2025.2476879 Item availability restricted.

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Abstract

The ability to rapidly and accurately identify high-value requirements and complete the conceptual design and performance validation of iterative products is crucial for companies to achieve swift product upgrades and enhance market competitiveness. This paper provides a comprehensive review of the latest methods and practices reported in the literature, covering various aspects such as requirement mining, scheme generation, and scheme validation in conceptual design. Furthermore, it proposes a framework for agile conceptual design and validation based on multi-source product data and Large Language Models (LLMs). The specific research focus areas include requirement mining using product usage data, AI-based conceptual scheme generation, and rapid conceptual scheme validation facilitated by physics-informed data-driven approaches. Finally, the paper discusses the challenges and future research prospects of agile conceptual design based on multi-source product data and Large Language Models.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Publisher:	Taylor and Francis Group
ISSN:	0954-4828
Date of First Compliant Deposit:	1 April 2025
Date of Acceptance:	5 March 2025
Last Modified:	01 Apr 2025 13:30
URI:	https://orca.cardiff.ac.uk/id/eprint/177125

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