# Conceiving a Digital Twin for a Flexible Manufacturing System
## 1 Introduction
### Background of Digital T twins: Concept and Integration Levels  
The term \"Twin\" was initially originated at NASA (National Aeronautics and Space Administration), through the simulated environment developed during the Apollo 13 mission, which consisted of building two identical space vehicles. While one spacecraft was launched into the air to carry out its mission, the other remained on the ground, allowing engineers to mirror the launch conditions [14]. Later, with technical improvements, the digital twin (DT) was adopted in the aerospace industry by NASA and the North American Air Force [15]. Consequently, the spacecraft kept on the ground was replaced by the digital replica to provide more information through high-fidelity simulations [16]. The DT initially consisted of three dimensions, including a physical part, a digital counterpart, and a connection for communication between the two parts [16; 17]. In 2010, NASA illustrated the definition and role of DT for space vehicles in detail in the Draft Modeling, Simulation, Information Technology and Processing Roadmap [18]. In the following year, the United States Air Force (US Air Force) developed the application of DT in the monitoring and management of the structural health of the aircraft [19]. In 2012, NASA and the US Air Force jointly published an article on DT, which stated that DT was the key technology for future vehicles [15]. After this publication, the amount of research on DT in the aerospace industry has grown continuously. For example, [PERSON] et al. [20] presented a multiphysics-stimulated DG simulation methodology for fleet management. According to [PERSON] et al. [21], DTs are able to moderate damage or degradation, activating a self-healing mechanism or 110 recommending changes in the mission profile, with the aim of reducing loads. One of the first DT works in the manufacturing area was proposed by [PERSON] et al. [22], as the virtual counterpart (stored in the Cloud) of production resources in the context of Industry 4.0. In 2014, the first white paper referring to the DT was published and the three-dimensional structure of the DT was widely disclosed [23]. Subsequently, DT was introduced in several areas besides the aerospace industry, such as oil and gas and medicine [16]. As of 2016, the term \"Digital Twin\" has been growing rapidly and becoming a \"hot\" topic among researchers, educators, software developers and professionals in the field [24]. In 2017 and 2018, Gartner deliberated DT as one of the ten technology trends for the next decade [25]. A DT can be defined as a digital model that reflects, in a timely manner, the state of a corresponding twin system based on historical data, real-time sensor data, and physical model [15]. A DT can mirror one or more resources of a physical environment, such as operators, machines, materials, and environments. The digital part is composed of functionalities for the purposes of data management, analysis, and computation [26; 27]. A DT is more than a simulation model [14; 23; 28; 29]. It is an intelligent and evolving model, being the virtual counterpart of a physical entity or process. It follows the life cycle of its physical twin to monitor, control and optimize its processes, continuously predicting its future states [10]. In general, DTs are useful for reducing production costs, improving and streamlining the test and production cycle of a product or process, reducing the time to introduce new products to the market, and creating a virtual environment in which all phases of the product are integrated [23]. The DT can add value to companies and many new business models can emerge from it [30].