Converting IEC 61131-3 LD into SFC Using Large Language Model: Dataset and Testing
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| Format: | Preprint |
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2025
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| _version_ | 1866909887765676032 |
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| author | Zhang, Yimin de Sousa, Mario |
| author_facet | Zhang, Yimin de Sousa, Mario |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12593 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Converting IEC 61131-3 LD into SFC Using Large Language Model: Dataset and Testing Zhang, Yimin de Sousa, Mario Programming Languages 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. |
| title | Converting IEC 61131-3 LD into SFC Using Large Language Model: Dataset and Testing |
| topic | Programming Languages |
| url | https://arxiv.org/abs/2509.12593 |