In the context of Industry 4.0, ensuring the compatibility of digital twins (DTs) with existing software systems in the manufacturing sector presents a significant challenge. The Asset Administration Shell (AAS), conceptualized as the standardized DT for an asset, offers a powerful framework that connects the DT with the established software infrastructure through interoperable knowledge representation. Although the IEC 63278 series specifies the AAS metamodel, it lacks a matching strategy for automating the mapping between proprietary data from existing software and AAS information models. Addressing this gap, we introduce a novel dual data mapping system (DDMS) that utilizes a fine-tuned open-source large language model (LLM) for entity matching. This system facilitates not only the mapping between existing software and AAS models but also between AAS models and standardized vocabulary dictionaries, thereby enhancing the model's semantic interoperability. A case study within the injection molding domain illustrates the practical application of DDMS for the automated creation of AAS instances, seamlessly integrating the manufacturer's existing data. Furthermore, we extensively investigate the potential of fine-tuning decode-only LLMs as generative classifiers and encoding-based classifiers for the entity matching task. To this end, we establish two AAS-specific datasets by collecting and compiling AAS-related resources. In addition, supplementary experiments are performed on general entity-matching benchmark datasets to ensure that our empirical conclusions and insights are generally applicable. The experiment results indicate that the fine-tuned generative LLM classifier achieves slightly better results, while the encoding-based classifier enables much faster inference. Furthermore, the fine-tuned LLM surpasses all state-of-the-art approaches for entity matching, including GPT-4 enhanced with in-context learning and chain of thoughts. This evidence highlights the effectiveness of the proposed DDMS in bridging the interoperability gap within DT applications, offering a scalable solution for the manufacturing industry.

中文翻译：

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双数据映射与微调大型语言模型和资产管理外壳，以实现可互操作的知识表示


在工业 4.0 的背景下，确保数字孪生 (DT) 与制造业现有软件系统的兼容性是一项重大挑战。资产管理外壳 (AAS) 被概念化为资产的标准化 DT，它提供了一个强大的框架，通过可互操作的知识表示将 DT 与已建立的软件基础设施连接起来。尽管 IEC 63278 系列指定了 AAS 元模型，但它缺乏用于自动映射现有软件的专有数据与 AAS 信息模型之间的匹配策略。为了解决这一差距，我们引入了一种新颖的双数据映射系统（DDMS），该系统利用微调的开源大语言模型（ LLM ）进行实体匹配。该系统不仅促进了现有软件与AAS模型之间的映射，而且促进了AAS模型与标准化词汇词典之间的映射，从而增强了模型的语义互操作性。注塑成型领域的案例研究说明了 DDMS 在自动创建 AAS 实例、无缝集成制造商现有数据方面的实际应用。此外，我们广泛研究了微调仅解码LLMs作为实体匹配任务的生成分类器和基于编码的分类器的潜力。为此，我们通过收集和编译 AAS 相关资源，建立了两个 AAS 特定数据集。此外，在通用实体匹配基准数据集上进行了补充实验，以确保我们的经验结论和见解具有普遍适用性。 实验结果表明，经过微调的生成式LLM分类器取得了稍好的结果，而基于编码的分类器可以实现更快的推理。此外，经过微调的LLM超越了所有最先进的实体匹配方法，包括通过上下文学习和思想链增强的 GPT-4。这一证据凸显了所提出的 DDMS 在弥合 DT 应用程序内互操作性差距方面的有效性，为制造业提供了可扩展的解决方案。