## 4 Analyses of the Applications of Digital Twin Models
### Manufacturing
#### 4.1.1 Application and Adoption of Digital Twin in Manufacturing
[56], [PERSON] and [PERSON] highlighted that the Digital Twin plays an important role in intelligent manufacturing in terms of equipment, manufacturing systems, and intelligent services. One of the advantages of using the Digital Twin in a production workshop is the real-time monitoring of the machines, products, and reflection of their behavior at the level of the digital model. Thus, the causes that lead to non-compliance of products with quality standards can be identified. In addition, the Digital Twin can contribute to optimizing the operation of devices by performing different simulation scenarios, thereby improving stability. With the help of the Digital Twin, users can be notified of the malfunctions that may occur at the level of physical assets, recommending a series of appropriate measures depending on the situation encountered. Among the limitations of the intelligent manufacturing production line as a result of the use of the Digital Twin, the realization of a large number of experiments to reduce costs and increase productivity was discussed. [PERSON] et al. [27] identified the advantages of using a Digital Twin in manufacturing as the design of the production system in the form of iterative organization, emphasizing the use of virtual evaluation and verification methods, redesigning existing physical objects, and improving product quality. Among the limitations is the presence of a low level of digital design to facilitate the creation of models with the help of a Digital Twin, as well as problems related to data integration. In ref. [57], [PERSON] and [PERSON] presented how the Digital Twin helps identify faults in a two-speed transmission system, which leads to cost reduction and quality assurance. Initially, three types of faults were defined, and then, an anomaly detection model was trained based on the system outputs. An accuracy of 92.7% was obtained, which leads to the recommendation of training the model with the help of generating a large number of data and features on the basis of which to identify anomalies. In ref. [58], one of the advantages discussed by [PERSON] regarding the use of Digital Twin in manufacturing consists of standardizing the prototype at the security level by implementing some risk prevention measures, but also in ensuring interoperability. The Digital Twin can also contribute to the development of digital models to ensure an efficient connection between different production locations, thus facilitating the efficient conduct of operations throughout the supply chain. Among the disadvantages mentioned, the emphasis is on the need to change the mindset of the staff regarding digital transformation in the manufacturing industry and to implement an organizational structure that supports the adaptation of the Digital Twin and modular testing. Some of the CPS challenges that can be solved using digital twins may arise from the cyber-security domain. It is recommended to carry out a risk analysis to avoid the appearance of vulnerabilities that can lead to unauthorized access to the system and data manipulation, which are affected by cyber-attacks. Additionally, data protection and privacy procedures must be implemented to prevent unauthorized access and data leakage. Using large volumes of data can lead to erroneous information management and analysis, which affects the modeling process. Thus, in such circumstances, the behavior of the system in the physical environment cannot be accurately reflected by the Digital Twin. In addition, when different equipment and technologies are used, their interoperability and integration can be complex, and problems can occur. The lack of digital twins and CPS interoperability during update operations can also cause problems that affect their adaptation and maintenance processes. [PERSON] et al. [59] presented an approach for identifying CPS anomalies using a Digital Twin (Adaptive Technologies for Integrated Industrial Networks--ATTAIN).