# Immersive Digital Twin under ISO 23247 Applied to Flexible Manufacturing Processes
## 5 Results and Discussion
The system allows for bidirectional communication between the entities, where after making the configurations either in the physical space (MPS-500 classification module) or virtual space (glasses or AR), the system begins the manufacturing process of products, and their behavior can be observed in a simulated way. Furthermore, in the digital system, simulations can be carried out to observe the behavior of the system or production that is going to be carried out prior to execution. One of the tests carried out to verify the operation of the DT consisted of generating controlled errors during the production line. The errors consisted of changing the color of the tokens manually, removing the token from the Cartesian arm, and stopping the distribution actuator. Subsequently, 20 tests were carried out for each of the proposed errors, both for the conventional system and with the immersive DT, under the same characteristics, and the average shown in Figure 9 was obtained. It is evident that there is an improvement of 17 s in the average time it takes the user to solve the error and continue with the production line; this is because in the DT, the operator can verify the status of the process with the data he constantly receives, and in addition, the DT generates an alert of where the error occurred and the location, also allowing for the control of some parameters from the AR environment, which helps in improving time. In the conventional process, the operator must visually verify where the error occurred and subsequently provide a solution, which increases production cycle times. Figure 8: (**a**) Virtual environment with AR and Senso Glove; (**b**) Physical and virtual environment of the process; (**c**) Process failure detection visualized in the 3D environment; (**d**) Virtual environment with AR. The analysis of the response times of the immersive DT was carried out based on the number of sensors and actuators because it was observed that having a greater number of connected devices resulted in a delay in the execution of the processes; this is due to the amount of data that must be processed and sent to the cloud so that it can later interact with physical and virtual environments. Subsequently, the time analysis was carried out based on the environment of the DT with AR and DT with Senso Glove, respectively, where the activation of several actuators was carried out and the number of actuators was increased to measure the response time. Activation was performed in the virtual environments and was measured based on the activation time in the physical environments. Figure 10 shows the response time for each environment, and it is evident that the DT with AR has a longer time compared to the Senso Glove. Therefore, it must be considered that for the implementation of these systems, they have a high consumption of resources, and despite using a high-end computer, there was a constant increase in time depending on the TAGs used. As seen in the previous figure, when executing the immersive environments with the DT, the times increase considerably, which causes delays in communication and lag times. However, these times only increase in immersive environments, while the data are displayed in real-time on the PC that runs the DT, which guarantees good performance. Because immersive environments present a delay in display and a high consumption of resources, the energy analysis was carried out with a Fluke analyzer, where it was carried out in analysis under three conditions, which were (1) system in stop, (2) conventional system, and (3) system with immersive DT. These tests were carried out for continuous time schedules, and the data were recorded with the analyzer. Subsequently, Power Log Classic software V4.6 was used, and the image in Figure 11 was obtained. When analyzing the data in Figure 11, it is observed that there is no considerable change in voltage in the  
Figure 10: Response time with AR and Senso Glove. Figure 9: Analysis of response time to errors caused by the user.