This research introduces a novel approach for assisting the creation of Asset Administration Shell (AAS) instances for digital twin modeling within the context of Industry 4.0, aiming to enhance interoperability in smart manufacturing and reduce manual effort. We construct a "semantic node" data structure to capture the semantic essence of textual data. Then, a system powered by large language models is designed and implemented to process "semantic node" and generate AAS instance models from textual technical data. Our evaluation demonstrates a 62-79% effective generation rate, indicating a substantial proportion of manual creation effort can be converted into easier validation effort, thereby reducing the time and cost in creating AAS instance models. In our evaluation, a comparative analysis of different LLMs and an in-depth ablation study of Retrieval-Augmented Generation (RAG) mechanisms provide insights into the effectiveness of LLM systems for interpreting technical concepts. Our findings emphasize LLMs' capability in automating AAS instance creation, enhancing semantic interoperability, and contributing to the broader field of semantic interoperability for digital twins in industrial applications. The prototype implementation and evaluation results are released on our GitHub Repository with the link: https://github.com/YuchenXia/AASbyLLM

翻译：本研究提出了一种创新方法，用于辅助创建面向工业4.0背景下数字孪生建模的资产管理壳（AAS）实例，旨在提升智能制造中的互操作性并减少人工工作量。我们构建了"语义节点"数据结构以捕捉文本数据的语义本质，进而设计并实现了一套基于大语言模型的系统，可处理"语义节点"并从文本技术数据中生成AAS实例模型。评估结果表明，该方法实现了62-79%的有效生成率，意味着大部分人工创建工作可转化为更便捷的验证工作，从而降低创建AAS实例模型的时间与成本。在评估中，我们对比分析了不同大语言模型的表现，并对检索增强生成机制进行了深度消融研究，揭示了LLM系统在解释技术概念方面的有效机制。研究结论表明，大语言模型具备自动化创建AAS实例的能力，可增强语义互操作性，为工业应用中数字孪生的语义互操作领域做出贡献。原型实现与评估结果已发布在GitHub仓库：https://github.com/YuchenXia/AASbyLLM