Digital Twin

The term digital twin is defined in different forms including as a high-fidelity simulation, a virtual organization, a virtual reality representation and an emulation facility. Its uses are in deploying optimization, monitoring, diagnostic, prognostic and prescriptive capabilities. The digital twin originated with the concept of a digital factory and is the digital representation of a physical asset or system, across its life-cycle, using operational real-time data and other sources, adopted to drive business outcomes.

The digital twin concept has been implemented by leading manufacturing companies. Ford Motor Company enhanced assembly line performance by evaluating and optimizing the designs using digital twins. Volvo Group Globalshowed how to validate changes using a digital twin. General Electric developed digital twins of aircraft engines. Major commercial software vendors support development of virtual factories via integrated solutions for product, process and system design, simulation, and visualization.

The recent concept of Industry 4.0, or the fourth industrial revolution, includes Cyber-Physical Systems (CPS) as a key component. The function of CPS is the monitoring of physical processes and creating a virtual copy of the physical world to support decentralized decision-making. In Industry 4.0 applications, one sees a growing role of twinning a physical plant with simulation-based surrogates. By means of sensors, real-time data about physical items are collected and used to duplicate the physical state of the item and assess the impact of ongoing changes. Digital twins include five main components: physical part, virtual part, connection, data, and service. The virtual and physical parts exchange information collected through the connection part. The interaction between the human and the digital twin is provided by the service part. Digital twins are traditionally used to improve the performance of engineering devices, like wind turbines or jet engines. In this context, they also serve to model systems of devices, to collect and analyze information about processes and people, and to help solve complex problems. Such digital twins provide powerful planning and troubleshooting capabilities and statistical methods play a significant role in both the design and analysis of simulations and computer experiments on digital twin platforms. Digital twin provide a platform that enables a life cycle perspective on products and systems. This emphasizes a transition from engineering the design to engineering the performance.