Digital twin driven smart factories: real time physics based co-simulation using edge a.i. and federated learning
V. Padmavathi, R. Kanimozhi, R. Saminathan
Abstract
Thanks to Industry 4.0, digital twin technology now leads the way in smart manufacturing, enabling the real-time replication of devices to facilitate maintenance, process improvements, and smooth operation. Still, many conventional Digital Twin approaches heavily rely on cloud solutions, which result in high latency, limited scaling options, and increased risks to data privacy. However, real-time, physics-enabled simulation remains very challenging for making fast and accurate choices in busy factories due to difficulties with computer speed and network capacity. We introduce a new method that combines Digital Twin, Edge AI, and FL to make co-simulation in smart factories quick, efficient, and secure. Moving the learning and simulation steps to edge devices near the factory floor enables real-time data processing and inference. Federated Learning enables these edge devices to share and learn from one another, ensuring that important production data remains within the factories. This engine, based on FMI, enables the real-time co-simulation of physical events with their virtual counterparts, allowing the system's behavior to be tested under various operating modes. The framework achieved a latency reduction of up to 35%, a 28% decrease in cloud usage, and a 13.2% throughput gain compared to cloud-only architectures, highlighting its scalability and industrial applicability. Additional ways to enhance this area include the use of blockchain, automated learning updates, and allowing machines to transfer knowledge from plant to plant.