Retrieval Augmented (Knowledge Graph), and Large Language Model-Driven Design Structure Matrix (DSM) Generation of Cyber-Physical Systems

2026-01-30

arXiv.org

AI Summary

本文探索了使用大型语言模型、检索增强生成及基于图的检索增强生成方法自动生成设计结构矩阵，以解决赛博物理系统中组件关系确定的问题。

Abstract

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.

Problem

• Design Structure Matrices
• Large Language Models
• Retrieval-Augmented Generation
• GraphRAG
• Cyber-Physical Systems

Innovation

• Large Language Models
• Retrieval-Augmented Generation
• Graph-based RAG
• Design Structure Matrices