As smart factories evolve, maintenance manuals need to be transformed from static documents into machine-readable and reusable digital assets. However, many legacy manuals are still in unstructured formats, such as Hangul word-processor files, which complicates their updating, reusability, and adaptability to changing product configurations. This paper presents a framework for converting these legacy manuals into S1000D-based documents. It combines style-based hierarchy extraction with rule-guided multi-step transformation using a local large language model (LLM). First, the style information within the Korean documents is analyzed to identify the hierarchical structure of the manual and extract content at various document levels. Next, this extracted content is converted into S1000D XML modules through the local LLM, utilizing category-specific rule files, XML tag definitions, and example templates. To enhance structural consistency and minimize errors, different prompts and rule sets are applied based on the document hierarchy level.A case study involving a maintenance manual for a high-angle limit switch module demonstrates that the proposed method can maintain document structure while generating reusable S1000D-style outputs from legacy technical documents. This approach lays a practical foundation for creating continuously updatable and context-reconfigurable maintenance guidance in smart manufacturing environments.
Um et al. (Mon,) studied this question.
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