An intelligent knowledge-driven module division framework for complex products

In modern product design, the modularisation of complex products plays a pivotal role by enabling greater design flexibility, reducing structural complexity, and improving overall manufacturability. Traditional methods of modularisation often rely heavily on expert knowledge and manual assessments, which can be time-consuming and error-prone. Despite increasing interest in automation, existing methods that integrate heterogeneous similarity and dependency metrics still suffer from limited automation and scalability, leaving room for more intelligent and efficient modularisation approaches. This paper proposes a new framework for the module division of complex products by integrating customised CAD tools, a Retrieval-Augmented Generation (RAG) pipeline supported by a Large Language Model (LLM), and a graph-based structural-attribute clustering algorithm. A case study on a bogie system demonstrates the feasibility of transforming dispersed design knowledge into quantifiable modular indicators, offering a practical solution for the automated modular design of complex products.