## 2 Related Work  
### Digital Twin and CPS  
In recent years, the number of devices connected to the local network and the Internet to form an intelligent system has increased significantly. This, in turn, aims to improve efficiency, agility, and user experience and support more autonomous decision-making [12]. In addition, these advances in information and communication technology have made it possible to collect and verify more detailed field data in real-time [13]. Using such data, the manufacturing environment is projected into virtual space to create a digital twin that reflects reality, the manufacturing process operation decision is made in that space, and the method of making operational decisions using big data evolves [14]. Recently, in addition to the CPS that enables real-time distributed control systems, research on digital twins, defined as virtual representations of physical assets created through data and simulators, continues. In Industry 4.0, the CPS and the digital twin are considered the key factors [15]. In particular, the digital twin creates a digital process based on physical process information in cyberspace and conducts learning for optimal process operation control by analyzing the movement of machines in the manufacturing process. In addition, by building CPS on the actual manufacturing process machine, the learning results obtained from the digital twin can be autonomously applied to industrial machines to strengthen collaboration between the real and virtual, and make the production process more flexible. As such, the connection between digital twins and CPSs can create strong synergies [16]. To support autonomous processes, data obtained from industrial processes must be processed quickly and accurately, and AI is a representative technology that can support the complete automation of the manufacturing process [17]. Existing systems in which people think, create, and adjust according to the manual are difficult to adapt to the dynamic environment that changes at a very fast rate [18]. In addition, to analyze the data of each process and place an accurate order for it in a timely manner, experienced consultants and managers are needed in real-time, which is expensive. Various recent studies on AI have shown that an automatic system can be implemented and show the possibility of it actually being used in the manufacturing field. Such smart manufacturing has a strong competitive edge in Industry 4.0 [19]. However, the adoption of AI technology has the drawback that it is difficult to understand and apply to actual processes due to the opacity of the decision [20].  
Figure 1 visually expresses the AI cooperation framework. Based on actual manufacturing process data, a digital twin is created in the virtual world, and the collected static operation data are learned through an AI algorithm to analyze the optimal operation scenario. Then, based on the CPS, it gives commands to the distributed actual process to solve problems, such as bottlenecks, that may occur in the manufacturing process. It provides explainable AI assistance to the worker to help them understand the behavior and consequences of the AI. Process operators can benefit from these comments to make faster and more accurate decisions [21].