A Framework for Digital Twin Deployment in Production Systems

Abstract

Digital twins represent physical systems through dynamic adaptive digital replicas. These replicas are virtual images of the functionality and interactions of the physical system and its components. Digital twin provides real-time monitoring and decision-making support. These are essential pillars in the Industry 4.0 paradigm. On the system level, multiple architectures are established for digital twin concepts in the literature. However, the roadmap for deploying a functional digital twin has not been fully recognized thus far. In this paper, we propose a framework for the deployment of a digital twin in production systems. The framework covers both levels of virtualization; digital shadowing, and digital twining. It utilizes present technologies in network communication, data management, and knowledge extraction. We describe the main components of the digital twin, and their relation to the management schemes currently implemented in production systems. Additionally, we devise an approach for integration of available simulation and data analytic tools for dynamic modeling and system performance evaluation. Through our proposed framework, the current technologies and tools are capable of deploying a digital twin. Consequently, learning algorithms and monitoring procedures are exploited for dynamic performance improvement.