LLM-based multi-agent system for autonomous maintenance process of machine tools

This paper presents a large language model (LLM)-based system for autonomous maintenance in manufacturing facilities. While many machine alarms are interpreted with existing manuals, understqanding and acting on these instructions of all facilities remains a challenge for operators. The proposed system processes user inputs including error codes, identifies corresponding procedures from manuals, and decomposes them into structured action sequences. These sequences include action, user interface target, preconditions, and expected outcomes, and are executed by agent capable of interacting with Human–Machine Interfaces (HMIs). The proposed system is built on an LLM-powered multi-agent framework comprising four agents: a chatbot, solution_finder, actor, and supervisor. Each agent operates based on role-specific prompts that define their responsibilities and decision rules. Instead of relying on predefined rule sets, the system interprets unfamiliar or previously unseen alarms by reasoning over machine manuals and context, enabling flexible and scalable maintenance. The system was implemented on a HMI system of CNC machine tools and successfully performed automatic responses to selected alarms. Prompt-based control ensure adaptability to other machines, and the use of a local LLM maintains data security. This approach enables general-purpose, self-directed maintenance with minimal operator intervention.