## 2 Related Works
The results showed that the proposed solution is suitable for the implementation and design of a physical device and allows the proposed method to be verified before the final implementation; in addition, it presents a flexible design, which ensures the agile development of various parts of the system and the development of individual tasks in parallel. In [22], they propose an architecture to merge SCADA systems with a DT with an architecture based on OPC UA with Ignition software and by adding an RPI card. The proposed architecture consists of a server, gateway, cloud service, and end-user nodes. The results showed that the architecture simplifies system connectivity and reduces the computation cost of process controllers by utilizing a gateway device to perform more demanding processing tasks. In [23], they present the integration of a DT in a production workshop that has Industry 4.0 technology, where it was considered to perform precise digital mapping with multidimensional, static, and multidimensional characteristics that describe the perspectives of geometry, physics, behavior, and rules. Production data, equipment operation data, production process parameters, and operation parameters were considered. The results showed that the proposed system provides an implementation method and approach for collaborative interaction and iterative operation problems caused by complex coupling in the process industry. In [24], they propose an online modeling and control method for complex assembly processes based on a DT in conjunction with a particle swarm optimization algorithm to dynamically adjust the pro cess parameters. The framework consists of three layers: a physical layer, a DT layer, and an optimal control layer. The physical layer consists of industrial robots, operators, and assembly parts. The DT layer provides high-fidelity, multi-physics, multi-scale simulation and prediction capability, and finally, the optimal control layer perceives and analyzes the real-time state of the assembly process through DT models in the DT layer to obtain the optimal parameters. considering the precision and efficiency of the assembly. The results show the superiority of the proposed approach compared to the traditional method because it significantly reduces the iteration time and improves the assembly quality. In [10], they propose a DT focused on self-development, and it will be implemented in an existing production system to develop data following the concept of Industry 4.0. In this way, the cost of starting a very low structure without automation is taken advantage of and the results are obtained in the form of data to analyze it to adapt it with automatic layering systems with maximum efficiency. The design was made based on the automation pyramid for data collection and sending them to the computer and HMI to be shown to the operator. These data are stored in MYSQL the Modbus TCP/IP protocol was used for communication, and through OPC, the data were sent to LabVIEW for real-time monitoring and communication with the HMI. In [25], they realize a DT-based distributed manufacturing system for Industry 4.0 with on-the-fly replanning capabilities powered by AI with the objective of autonomously verifying, interpreting, and executing production plans. The proposed methodology executes the initial plan while the AI searches for more efficient alternatives and sends better solutions to the MES, which updates the new plan during production. The results are shown and validated in a use case using an Industry 4.0 testbed, which is equipped with an automated transport system, renewable energy sources, and KUKA industrial robots. The results showed that it is feasible to encapsulate the digital twin (basic digital twin based on the PDDL model) with a scheduler into a new enhanced digital twin (digital twin with AI scheduler), combining the advantage of rescheduling capabilities with a distributed MES.