AutoFactory Dataset to Support AI in Manufacturing Systems

Automating code generation in manufacturing systems requires Artificial Intelligence (AI) models capable of interpreting textual requirement specifications. One of the main challenges is the absence of publicly available, domain-specific datasets suitable for training such models. This article presents AutoFactory, an open-source dataset that includes manually written and LLM-augmented requirement specifications, annotated by domain experts for Named Entity Recognition (NER) tasks using the BIO format. AutoFactory enables the extraction and classification of key industrial components, including actuators, pre-actuators, sensors, effectors, and others. These elements correspond to real-world input and output signals in manufacturing systems. The dataset is grounded in realistic scenarios built with Factory I/O and includes 3D scenes, tag tables, and screenshots captured from multiple camera perspectives. To enhance linguistic diversity while preserving semantic intent, all specifications were expanded using large language models and evaluated through semantic similarity analysis (average cosine similarity above 0.92), complemented by manual validation to ensure consistency. To facilitate the annotation process, a custom labeling tool was developed. It runs locally to preserve data privacy and provides a user-friendly interface. The tool is adaptable to a wide range of NER tasks and allows researchers to customize labels for different domains by adding or modifying tags. To assess the practical value of the dataset, transformer-based models such as BERT and DistilBERT were fine-tuned on the annotated requirement specifications. The highest-performing model reached an F1-score of 0.95, confirming the dataset's ability to support accurate identification of key components in manufacturing systems. This combination of human expertise and AI-based augmentation provides a robust foundation for training AI systems to interpret manufacturing behaviors from text. AutoFactory contributes to ongoing research efforts aimed at enabling intelligent systems to assist in generating control code from requirement specifications, reducing reliance on manual programming in industrial automation.