Recognizing and Integrating Legacy Assembly Diagrams into Industry 4.0

Legacy assembly diagrams, often provided as machine-unreadable images, hinder integration into the Industry 4.0 (I4.0) ecosystem. We propose a multi-step pipeline leveraging Large Language Models as a single simple-to-operate tool to recognize parts, relationships, and global identifiers, segment parts in images, and structure the data into Asset Administration Shell as I4.0 digital twins. Targeted prompts are designed for each step and evaluated on 15 diverse real-world diagrams. Results show that while the LLM reliably recognizes parts and link identifiers, there are still some open challenges with relationship extraction and semantic segmentation. Despite these limitations, LLMs provide a viable tool for semi-automated digitalization. Our end-to-end pipeline thus enables seamless integration of legacy diagrams into I4.0 systems.

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Published
2025-10-14
DOI
https://doi.org/10.1109/iecon58223.2025.11221583
Primary Topic
Digital Transformation in Industry
Type
article
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article

Recognizing and Integrating Legacy Assembly Diagrams into Industry 4.0

Marko Ristin, Hans Wernher van de Venn, Björn Otto, Nico Braunisch et al.
Digital Transformation in Industry
article

Recognizing and Integrating Legacy Assembly Diagrams into Industry 4.0

Marko Ristin, Hans Wernher van de Venn, Björn Otto, Nico Braunisch, Martin Wollschlaeger, D.Yu. Serikov, Marcin Sadurski
article en

Abstract

Legacy assembly diagrams, often provided as machine-unreadable images, hinder integration into the Industry 4.0 (I4.0) ecosystem. We propose a multi-step pipeline leveraging Large Language Models as a single simple-to-operate tool to recognize parts, relationships, and global identifiers, segment parts in images, and structure the data into Asset Administration Shell as I4.0 digital twins. Targeted prompts are designed for each step and evaluated on 15 diverse real-world diagrams. Results show that while the LLM reliably recognizes parts and link identifiers, there are still some open challenges with relationship extraction and semantic segmentation. Despite these limitations, LLMs provide a viable tool for semi-automated digitalization. Our end-to-end pipeline thus enables seamless integration of legacy diagrams into I4.0 systems.

ZHAW Zurich University of Applied Sciences (CH), Nazarbayev University (KZ), Technische Universität Dresden (DE), Otto-von-Guericke-Universität Magdeburg (DE)
Openalex Percentile: Top 54%
Digital Transformation in Industry
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Recognizing and Integrating Legacy Assembly Diagrams into Industry 4.0 — Marko Ristin, Hans Wernher van de Venn, et al. · (2025) | TGRS Research Map | TGRS