Intelligent extraction of manufacturing system components from natural language using transformer models
提出了AutoFactory数据集和AutoLabel-NER标注工具，将制造系统组件提取视为命名实体识别任务，微调多个Transformer模型，并构建了从提取组件到生成IEC 61131-3结构化文本代码的流水线。
Writing control programs for manufacturing systems is time-consuming and requires expert knowledge. Traditional methods, whether heuristic or formal, often fail to scale with the complexity and flexibility of modern production systems. A key challenge limiting progress is the lack of labelled data tailored to the domain of control logic. This gap hinders the development of AI systems capable of automating control program generation from natural language specifications. In this work, we introduce AutoFactory, a new dataset specifically designed for this task, along with AutoLabel-NER, a custom labelling tool that supports efficient annotation. We frame component extraction as a Named Entity Recognition problem and fine-tune several transformer-based models to evaluate their ability to detect relevant entities in requirement specifications. We benchmark thirteen transformer-based models on this dataset and compare their performance. Our results show that language models can detect control components accurately. To continue toward automation, we propose a pipeline that maps extracted components to predefined local control logic blocks. This pipeline generates IEC 61131-3 Structured Text code without manual programming. Our work creates the first benchmark and method for AI-assisted control program generation. It provides a solid foundation for future research in intelligent industrial automation.