LLM-Enhanced Human-Machine Interaction for Adaptive Decision-Making in Dynamic Manufacturing Process Environments

Modern production systems generate vast amounts of process data that hold valuable insights for optimizing manufacturing processes. However, production personnel often face the challenge of interpreting this information, especially when dealing with unexpected anomalies or when insights beyond standard reports are required. This challenge arises both from the complex data structures in which the data is provided, and the lack of analytical expertise. This research introduces an approach that leverages Large Language Models (LLMs) to facilitate natural language queries and flexible data visualization, allowing production personnel to interact effortlessly with complex datasets. Tested on process data from an industrial extrusion process that has been enhanced using data augmentation techniques, the proposed concept demonstrates the capability to retrieve relevant data and present tailored visualizations based on simple user prompts. The results demonstrate that LLM-driven data exploration can support production personnel and help overcome the challenges described, which arise from the complex nature of manufacturing data and the specialized domain knowledge required. Future work will concentrate on improving accuracy, robustness, and further integration of domain-specific knowledge, aiming to provide a more reliable and accessible tool for various industrial environments.