## 3 Digital Twin, from Concept to Architecture Model
### Classification of Digital Twins
* _Digital Twin:_ This DT category involves a two-way data transmission between the physical twin and the digital one, and any modification made to both the physical and the digital object will be reflected in the behavior of its counterpart. * _Predictive Digital Twin:_ Like the normal Digital Twin, it is characterized by continuous, real-time communication between the two components. The novelty brought by the Predictive DT consists of the fact that the digital twin also contains cyber security components and artificial intelligence algorithms that increase the accuracy of the simulation results and allow predictions to be made. Figure 7: Classification of DTs. According to [12; 14], a division of DT can be made according to the parameters on which the application focuses, such as the product, process, and performance:  
* _Product DT:_ DT is used for prototyping, and various conditions are analyzed, which helps to confirm that the physical product behaves according to the desired standards. * _Production DT:_ DT is used before actual production and has a role in process validation, simulation and analysis. It also helps develop efficient production methodologies. * _Performance DT:_ This DT can include both actual product and production performance and is used for decision-making processes by receiving, integrating, and analyzing product data. It also optimizes operations based on resource availability, providing the opportunity to improve product and production DT through a feedback loop. From a hierarchical perspective, DT can be structured on three levels. * _Unit level:_ Represents the smallest unit participating in production and examines the functional, geometric, behavioral, and operational model of the physical counterpart unit level. * _System level:_ Represents a collaboration of unit-level DTs, and each unit-level DT represents a component of the new system. * _System of Systems (SoSs) level:_ Represents the connection of multiple system-level DTs to form a system of systems. The system-of-systems DT also includes various phases throughout the life cycle of a product. Depending on the maturity level, a classification of DTs was proposed in [15] as follows:
* _Partial DT:_ May contain parameters (pressure, temperature, humidity) and is used to determine the DT functionality and connectivity. * _DT Clone:_ It is used to make prototypes that are made by means of the relevant data about the product/system. The data are contained in this DT. * _Augmented DT:_ It has the role of making a correlation between current data and past data based on algorithms and analyses. According to [16], [PERSON] et al., proposed a classification of the DT not only according to the level of maturity of the data but also according to the level of complexity of the virtual model as follows:
* _Pre-Digital Twin:_ In this stage, the DT is created before the physical object and is used in the process of decision making, referring to the prototype to reduce the risks that may arise. * _Digital Twin:_ It represents the second stage, and at its level, the data of the physical product are incorporated. It is applied in the design and development decision-making phases of the product life cycle, and data transfer to it is realized in both senses. * _Adaptive Digital Twin:_ The DT offers a dynamic interface between the DT itself and the physical object. It has the ability of priority learning and can maintain the human operators preferences with the help of supervised machine learning process. Operation's real-time decision-making and planning are also noted as advantages. * _Intelligent Digital Twin:_ Unlike the Adaptive DT, this DT offers unsupervised machine learning functionality, increasing its autonomy. It also provides higher accuracy and efficient system analysis. Table 2 shows the classification of DTs according to the previously mentioned criteria together with the corresponding bibliographic references.