Retrieval Augmented (Knowledge Graph), and Large Language Model-Driven Design Structure Matrix (DSM) Generation of Cyber-Physical Systems
This work addresses the problem of automating DSM generation for engineers in cyber-physical system design, but it is incremental as it builds on existing LLM and RAG methods.
The paper tackled automated generation of Design Structure Matrices (DSMs) for cyber-physical systems using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and GraphRAG, testing on a power screwdriver and a CubeSat to determine component relationships and identify components, with all code made publicly available.
We explore the potential of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Graph-based RAG (GraphRAG) for generating Design Structure Matrices (DSMs). We test these methods on two distinct use cases -- a power screwdriver and a CubeSat with known architectural references -- evaluating their performance on two key tasks: determining relationships between predefined components, and the more complex challenge of identifying components and their subsequent relationships. We measure the performance by assessing each element of the DSM and overall architecture. Despite design and computational challenges, we identify opportunities for automated DSM generation, with all code publicly available for reproducibility and further feedback from the domain experts.