# Digital Twin for Designing and Reconfiguring Human-Robot Collaborative Assembly Lines
## 4 Case Study
#### Results  
Regarding the design of the production system, Discrete Event Simulation (DES) has been used for calculating the performance of the DT-based generated design assembly paradigm against current practice. Two different DES models were deployed in WITNESS Discrete Event Simulation Software, version 23 [33]. The first model represented the current assembly line structure, modeling the execution time for each workstation. The tasks involved in each workstation were executed by human operators and by a compression machine. The execution time for these tasks was modeled based on relevant data provided by the automotive end user. The second model simulated the new set up of the production system that employs mobile robots to support human operators. The structure of the second model was designed based on the outcome of the DT-based system. This model as well incorporated the execution time of the tasks performed by the mobile robots, the human operators, and the compression machine. The execution time for human operators and the compression machine was modeled using the same source of information as in the first model. For modeling the time required for the mobile robots to execute their assigned tasks, the execution of the tasks by the robots was simulated in GAZEBO 3D simulator recording their duration. The simulation time for both models was one year. The production throughput and the utilization of resources were recorded for both models during this simulated year of production. The results, shown in Table 2, indicate a considerable improvement in  
Figure 11: The selected configuration physically deployed. the production throughput and utilization of operators' availability by facilitating the reconfigurable HRC system. Regarding the re-design of the system, below, we present the responsiveness of the system in two cases of execution disturbances against current industrial practice. Figure 12 visualizes the 3D planning scene reconstructed by the DT based on the physical world real-time status. This 3D planning scene is generated by the combination of data captured by two laser scanners placed on the mobile platform and one stereo camera placed on the robot's torso. By combining this information, the human presence and state is identified and included in the planning scene. Case 1: The operator enters the common workspace, and his path intersects the mobile robot's planned trajectory for 10 s. In current industrial practice, the mobile robot would stop, waiting for the human to move away and needing 1 s to resume its path. In the same case, the DT-based system will detect his NO INTERACTION intention and add human presence and state information in the planning scene. The mobile robot, instead of stopping, will re-plan its path in around 2 s, as shown in Figure 13. To quantify this effect, in an 8 h shift, we consider that the human will block the robot path 200 times. As shown in Table 3, the latter practice may reduce by 80% the overall delay caused by human presence. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**KPI** & **Current** & **DT Based Design** & **Increase** \\ \hline Annual production & & & \\ throughput—No. of Parts & 315,353 & 394,997 & 25\% \\ \hline Time percentage allocated to & & & \\ performing assembly (\%) & 34 & 49 & 30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the two systems—designed system performance. Figure 12: Automotive pilot cell (**a**) real world, (**b**) 3D planning scene. Figure 13: Human NO INTERACTION intention detected. Case 2: Robot arm fails to grasp the damper. In a realistic set up, the robot may fail to execute a task due to occlusions in the cameras' field of view, lighting conditions, etc. Through experimentation, we consider that in an 8 h shift, the robot fails 50 times.