Posts

paper at NAMRC conference

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Anuj Gautam , Manish Raj Aryal, Sourabh Deshpande, Shailesh Padalkar, Mikhail Nikolaenko, Ming Tang, Sam Anand. IIoT-enabled Digital Twin for legacy and smart factory machines with LLM integration. 53rd SME North American Manufacturing Research Conference (NAMRC), Clemson Univ. 06/2025.

The paper is also accepted in the  Journal of Manufacturing Systems 

Abstract

The recent advancement in Large Language Models (LLMs) has 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. The 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.

Digital Twin of Cincinnati

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A realtime flythrough demo for Digital Twin of City Cincinnati

Digital Futures Building at the University of Cincinnati

Destroy Alien buildings near the UC campus. Project developed by students Cooper Pflaum and Nishanth Chidambaram. 

Protected: Digital Twin, LLM & IIOT

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Cloud-based Digital Twin

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Clients are one click away from interacting with a Digital Twin model on their personal devices. No installation is required.

The XR-Lab’s project showcases a cloud-based Digital Twin (DT) model, designed for accessibility and interaction via mobile devices. This advanced DT model allows multiple users to engage with its complex features directly through touch screens, eliminating the need for app installations. Clients can effortlessly access the content using a simple URL in a web browser on their personal iOS or Android mobile devices and tablets. The project is distinguished by its photorealistic renderings, which are streamed to clients at high frame rates, ensuring a visually rich and seamless experience. Furthermore, our DT model is an integration of various cutting-edge technologies, including Building Information Modeling (BIM), Metadata, IoT sensor data, 360-degree images/videos, and web3D content, creating a comprehensive and interactive digital environment.

 

More information on Future of Work: Understanding the interrelationships between humans and technology to improve the quality of work-life in smart buildings.