## 4 Analyses of the Applications of Digital Twin Models
### Manufacturing
#### 4.1.1 Application and Adoption of Digital Twin in Manufacturing
[PERSON] et al. [49] proposed a complex Digital Twin (DT) modeling method based on model division, assembly, and standardized processing. The first stage of the process is represented by dividing the complex model of the DT into different models according to context, composition, code, and modules in the 4C (Composition-Context-Component-Code) architecture. Four characteristics of complex digital twins were also specified: interoperability, extensibility scalability, and fidelity. Composition and context have the role of influencing the digital twin to consider, in a specific scenario and scale, effective elements, and components. The digital twin-associated code was developed in standard-based modularization. The next stage is the assembly of simple DT models into high-complexity models through a knowledge graph, ontology, and analysis of multi-context scenarios. Efficient data update methods are used to achieve information fusion as static data, which are updated only when the DT is initiated, or real-time updated and dynamic data. The knowledge graph realizes structural correlations among the various scales of simple DTs and records the necessary attributes of the physical entities. Cyber-Physical Systems (CPSs) are systems that use sensors and execution elements in connection with physical processes, acquire and analyze recorded data, and actively and reactively change information with the physical and virtual world, according to [50]. In ref. [51], the idea was circulated that a Digital Twin can be used to safely monitor and control human-robot assembly operations, performed in a collaborative mode, and ensure this interaction at a distance. In Refs. [52; 53], DT was used for HRC modeling, reconfiguration, and collaborative assembly. By realizing the robot CPS-based digital twin and the human operator, it is possible to control the real robot or verify the movement of the real robot, as well as to compute the real-time safety distance. Following a study carried out by Oracle [54], several elements were identified that increase the value of objects/processes through the use of DTs, among which we mention the following:* Remote control and monitoring in real-time: the digital twin can be monitored and controlled at any time from any location. * Increased safety and efficiency: integrating quantitative data and performing complex analyses in real time. * Predictive maintenance and planning, achieved by continuously analyzing data and taking measures so that the impact of failures is minimal or eliminated. * Assessment of risks through simulations to take place at the level of the digital twin and finding methods to mitigate risks highlighted from a primary phase of the life cycle. * Rapid and continuous customization of products and services according to current trends and customer requirements. In ref. [55], [PERSON] and [PERSON] identified how Digital Twin and big data technologies complement each other, bringing synergy and helping promote intelligent manufacturing. The advantages discussed are product design, production planning, and predictive maintenance. In addition, by means of simulations carried out at the level of digital models, the elements that contribute to the improvement of operation, adaptability to change, and estimation of the product's life span can be identified. The Digital Twin not only helps to accurately identify design defects in the virtual environment, but also to adjust the manufacturing process to achieve optimal production that ensures non-functional requirements such as precision, stability, high efficiency, and quality. Among the challenges mentioned by [PERSON] and [PERSON], comprehensive studies on the application of Digital Twins in smart manufacturing have a high level of complexity. It is also recommended to carry out exhaustive research to ensure the accuracy of models in manufacturing, as a result of data integration, fusion algorithms, existing models, and platforms. In ref.