In this section
@article{Wang:2025:10.1016/j.aei.2025.103427,
author = {Wang, B and Zhao, X and Zuo, H and Song, Y and Han, J and Childs, P and Chen, L},
doi = {10.1016/j.aei.2025.103427},
journal = {Advanced Engineering Informatics},
title = {From analogy to innovation: a creative conceptual design approach leveraging large language models},
url = {http://dx.doi.org/10.1016/j.aei.2025.103427},
volume = {67},
year = {2025}
}
TY - JOUR
AB - Integrating creative concepts into Product Design and Manufacturing Systems (PDMS) is important for product innovation. However, current PDMS lack cognitive capabilities, particularly in reasoning and synthesis, which are essential for conceptual design. As a result, designers face challenges in retrieving relevant analogies, establishing meaningful mappings, and integrating knowledge into new design concepts. This paper proposes a computational conceptual product design approach that integrates Large Language Models’ (LLMs) knowledge representation with an analogy-based structured retrieval mechanism, supporting designers to explore and recombine design patterns and functionalities in an intuitive manner. Benefiting from the zero-shot learning and prompting capabilities of LLMs, given a source domain, this approach allows reasoning target domain based on abstract correspondences in both morphological and semantic associations. A template for the combinational regulation of the reuse of analogical knowledge has also been formulated. By decomposing analogical knowledge into ontological distinction, inspirational feature recognition, and associative mapping explanation, a creative conceptual design stimulation path is formed. An interactive tool named ViMimic based on this approach has been developed through a case study with 18 participants. Evaluation results demonstrate that the approach improves creative performance, increasing the novelty and functionality of conceptual designs by 51% and 22% respectively according to expert evaluations. It also boosts the efficiency and diversity of analogy mapping by 30% based on objective measures, while enhancing creative experiences and reducing cognitive load as measured in the participants’ self-assessment.
AU - Wang,B
AU - Zhao,X
AU - Zuo,H
AU - Song,Y
AU - Han,J
AU - Childs,P
AU - Chen,L
DO - 10.1016/j.aei.2025.103427
PY - 2025///
SN - 1474-0346
TI - From analogy to innovation: a creative conceptual design approach leveraging large language models
T2 - Advanced Engineering Informatics
UR - http://dx.doi.org/10.1016/j.aei.2025.103427
UR - https://doi.org/10.1016/j.aei.2025.103427
VL - 67
ER -
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