IIoT-enabled digital twin for legacy and smart factory machines with LLM integration

Recent advancements in Large Language Models (LLMs) have significantly transformed the field of natural data interpretation, translation, and user training. However, a notable gap exists when LLMs are tasked to assist with real-time context-sensitive machine data. The paper presents a multi-agent LLM framework capable of accessing and interpreting real-time and historical data through an Industrial Internet of Things (IIoT) platform for evidence-based inferences. Real-time data is acquired from several legacy machine artifacts (such as seven-segment displays, toggle switches, and knobs), smart machines (such as 3D printers), and building data (such as sound sensors and temperature measurement devices) through MTConnect data streaming protocol. Further, a multi-agent LLM framework that consists of four specialized agents – a supervisor agent, a machine-expertise agent, a data visualization agent, and a fault-diagnostic agent is developed for context-specific manufacturing tasks. This LLM framework is then integrated into a digital twin to visualize the unstructured data in real time. The paper also explores how LLM-based digital twins can serve as real time virtual experts through an avatar, minimizing reliance on traditional manuals or supervisor-based expertise. To demonstrate the functionality and effectiveness of this framework, we present a case study consisting of legacy machine artifacts and modern machines. The results highlight the practical application of LLM to assist and infer real-time machine data in a digital twin environment. Peer-review under responsibility of the scientific committee of the NAMRI/SME. • Legacy and smart machines data integration into a context-aware, high-fidelity digital twin. • Real-time, monitoring and process optimization of environmental and machine IIoT data. • Multi-agent LLM enhances machine control and provides expert-level insights with voice assistance and avatar. • Fault Diagnosis Agent optimizes 3D printing in real time, reducing material waste and improving print quality.