In this section
@inproceedings{Cai:2025:10.1115/detc2025-165946,
author = {Cai, Z and Liu, R and Jing, Q and Zuo, H and Sun, L and Childs, P and Chen, L},
doi = {10.1115/detc2025-165946},
pages = {1--10},
publisher = {American Society of Mechanical Engineers},
title = {An automated functional decomposition method based on large language models},
url = {http://dx.doi.org/10.1115/detc2025-165946},
year = {2025}
}
TY - CPAPER
AB - Functional decomposition in conceptual design aims to break down the overall function of a complex product or system into smaller, implementable subfunctions. This process enables designers to systematically explore solution spaces, improve problem structuring, and manage design complexity. However, traditional functional decomposition presents substantial cognitive challenges, as designers often struggle to identify functionalities at suitable abstraction levels, adhere to structured syntax format requirements, and maintain solution neutrality. Such difficulties may result in inconsistencies in decomposition quality and inefficiencies throughout the design process. To mitigate these challenges, this research proposes an automated functional decomposition method leveraging a Large Language Model (LLM). A comprehensive function basis is constructed from a large-scale dataset of award-winning product and concept designs. This function basis serves as the vocabulary base for the automated decomposition process. The proposed method iteratively employs an LLM-powered functional decomposer to generate subfunctions while integrating a filtering mechanism to screen outputs and determine whether decomposition is complete. A comparative experiment evaluating designer-generated versus LLM-generated function trees demonstrates that the proposed method significantly enhances functional ideation breadth and improves decomposition quality and efficiency. The findings suggest that LLM-assisted decomposition can effectively support conceptual design, offering a scalable and systematic alternative to traditional manual approaches.
AU - Cai,Z
AU - Liu,R
AU - Jing,Q
AU - Zuo,H
AU - Sun,L
AU - Childs,P
AU - Chen,L
DO - 10.1115/detc2025-165946
EP - 10
PB - American Society of Mechanical Engineers
PY - 2025///
SP - 1
TI - An automated functional decomposition method based on large language models
UR - http://dx.doi.org/10.1115/detc2025-165946
UR - https://doi.org/10.1115/detc2025-165946
ER -