## 2 Related Work  
### Digital Twin Overview  
With the transition to Industry 4.0, technologies such as data collection, storage, and communication have developed rapidly, and these developments have made it possible to check the field conditions through data [2]. This means that a digital twin using data from the manufacturing system can be linked with the real environment in real time [3]. Digital twin models created using these data can help solve traditional elusive problems by mirroring manufacturing environments. With the development of cloud computing, IoT technology, and manufacturing systems, a new paradigm for making decisions based on big data has emerged [4]. Recently, there is a growing trend of research on the implementation of digital twins for physical assets of smart factories, and Industry 4.0 aims to develop a smart factory that can meet the rapidly changing needs of customers at a lower cost [5]. In this environment, digital twins provide better decision-making and prediction in manufacturing, as well as meaningful insights into decision-making in the manufacturing process [6]. Digital twin technology can also reduce the potential risks associated with planning manufacturing processes without physical awareness. Digital twin related researchers predict that, after reducing these risk factors, many of the major industry players will utilize digital twin technology to increase manufacturing efficiencies by 10. Early models of digital twins were used for the manufacture and repair of aircraft with numerous parts. Digital twins were also a technology that could predict the lifespan of an aircraft, which directly affects many people by maintaining the integrity of the product. A conceptual model that can predict the lifespan of aircraft structures has additionally been presented [7]. Since then, research to use digital twins in the manufacturing field has been continually conducted, including on digital twin configurations using physical workspaces, virtual workspaces, and data [8]. In addition, [PERSON] showed that there is an advantage to creating digital twins through real time data collection and applying decisions made based on this approach to actual SMEs [9]. In the manufacturing process, digital twins can directly run and review things that cannot be executed at the actual manufacturing site through the virtual manufacturing process. Additionally, they help with operation during the manufacturing process through various insights gained experimentally. Figure 1 is a visual representation of the digital twin framework. Real-time data created during the manufacturing processes of smart factories implemented in the real world are collected through multiple sensors, and interpretation of these data is used to make decisions by considering big data. Using big data, the machine learning process is carried out, and the digital twin model created through this process is used to implement scenarios that cannot be applied in the real world due to excessive costs. As shown in the  
figure, the results of the scenario are transmitted to the decision maker for the smart factory manufacturing process for decision-making, and the decision maker can use the indicator to provide more appropriate and efficient instructions. Physical asset information can be collected in real time with the development of IoT technology, and this information is shared in a cloud computing environment, leading to a digital transformation that can improve flexibility in product creation. To enable real time interworking of such information, some degree of abstraction is required to calculate the actual phenomenon [10]. In addition, stable data processing and transmission speeds are required to provide advice and insight into decision-making through the mirror model, where this technology can bridge the physical and digital worlds [11]. Research on an environment that can handle digital twins is needed such as cloud computing [12].