Converting IEC 61131-3 LD into SFC Using Large Language Model: Dataset and Testing

In the domain of Programmable Logic Controller (PLC) programming, converting a Ladder Diagram (LD) into a Sequential Function Chart (SFC) is an inherently challenging problem, primarily due to the lack of domain-specific knowledge and the issue of state explosion in existing algorithms. However, the...

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Veröffentlicht in:Proceedings (IEEE International Conference on Emerging Technologies and Factory Automation) S. 1 - 8
Hauptverfasser: Zhang, Yimin, De Sousa, Mario
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 09.09.2025
Schlagworte:
Accuracy
Explosions
Few shot learning
Fine-tuning
IEC 61131
IEC Standards
Ladder Diagram
Large Language Model
Large language models
Manufacturing automation
Programmable Logic Controller
Programmable logic devices
Programming
Sequential Function Chart
Testing
Training
ISSN:1946-0759
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Zusammenfassung:In the domain of Programmable Logic Controller (PLC) programming, converting a Ladder Diagram (LD) into a Sequential Function Chart (SFC) is an inherently challenging problem, primarily due to the lack of domain-specific knowledge and the issue of state explosion in existing algorithms. However, the rapid development of Artificial Intelligence (AI) - especially Large Language Model (LLM) - offers a promising new approach.Despite this potential, data-driven approaches in this field have been hindered by a lack of suitable datasets. To address this gap, we constructed several datasets consisting of paired textual representations of SFC and LD programs that conform to the IEC 61131-3 standard.Based on these datasets, we explored the feasibility of automating the LD-SFC conversion using LLM. Our preliminary experiments show that a fine-tuned LLM model achieves up to 91% accuracy on certain dataset, with the lowest observed accuracy being 79%, suggesting that with proper training and representation, LLMs can effectively support LD-SFC conversion. These early results highlight the viability and future potential of this approach.
ISSN:1946-0759
DOI:10.1109/ETFA65518.2025.11205542