# Digital Twins: Enabling Interoperability in Smart Manufacturing Networks
## 3 Digital Twins
### Applications of Digital Twins  
Digital twins can also be classified as per their applications. According to [PERSON], [12] the applications are also broadly divided into two categories, prediction and interrogation. In a predictive DT, future behaviour and performance of its physical copy are speculated. While in interrogative DT, the past and present state of the physical copy is examined. DTs can also be classified as per the focus of the application, which is either product, process, or performance [4; 15; 16]:  
* Product DT analyses the product in different conditions and ensures that the physical product is acting as expected. This virtual validation of the product leads to rapid prototyping and reduced development time.
* Production DT is used to validate the processes through simulation and analysis, before beginning actual production, which paves the way for the creation of a flexible production approach. The product and production DT data can be utilised to track and maintain the equipment.
* Performance DT is used for decision-making through data collection and analysis. Performance DT incorporates product and production performances and therefore it optimises the functioning of the industry floor according to the obtainability of the industry resources. This creates an option to boost the performance of both production and product DT using a feedback loop.  
The sophistication level of digital twins can be expressed in terms of autonomy, intelligence, learning and fidelity. According to the quantity and quality of data collected from the physical twin and its surroundings, DTs can have multiple properties:  
* Partial Digital Twins consist of only a small amount of data from their physical counterpart.
* Clone Digital Twins consist of a significant amount of data from the physical system useful for making prototypes.
* Augmented Digital Twins use the collected data from the asset along with the historical data and derive useful information using data analysis.  
Finally, when classifying DTs, the sophistication level of the virtual representation of the DT model allows for DTs to be further divided into four levels.  
* Pre-digital Twin is the first level where the DT is created before the physical system to analyse prototype designs and rule out any technical risks by virtual commissioning.
* Digital Twin is the second level when the data is collected from the physical copy relating to the performance, robustness, and maintenance. The virtual model uses the collected data to assist in the design and development of the physical system along with decision-making and arranging maintenance.
* Adaptive Digital Twin is third level which imparts an adaptive interface between the physical and the digital world. Using ML techniques, it learns from the experiences of the human operators, allowing for real-time decision-making.  
* Intelligent Digital Twin has additional capabilities along with features from the second and third levels. It can detect patterns in the manufacturing floor using reinforcement learning, allowing for more precision and efficient handling of the system.