## 4 Implementation and Results
### Results  
BOM data is extracted from CAD files created by inventory through VR Parser, converted to Json, and delivered to the CPS Server. At this time, the BOM data of the CAD file modified in the inventory is well transmitted to the CPS server, and the BOM data from the VR Parser checks the accuracy of the configuration of the address space of the CPS Server in real time. Further, the FBX file extraction function for external program interworking compresses the data in the CAD file to measure the capacity optimization (capacity reduction) value. The test environment was constructed as depicted in Figure 16.  
The total number of CAD files to be converted is 5, and the total number of data objects is 77. After running the inventory, the CAD file should be opened to check the CAD DOM data. The VR Parser should then be run to load CAD files. As shown in Figure 17, UA Expert is a representative client that supports the OPC UA protocol with software from a company called Unified Automation. The BOM data of the inventory, the number of nodes converted to OPC UA, Node name, Node tree structure, and Node properties can be verified by running the most commonly used internationally test client that supports OPC UA functions such as data access, alarms, history, and UA method calls. This procedure is executed by the number of CAD files, and the accuracy converted to the expression of (1) is calculated. \[\text{Conversion accuracy}=\frac{\text{Number of Objects Converted to Address Space}}{\text{CAD File Total Objects}}\times 100 \tag{1}\]  
As a result of the data conversion test, the accuracy of address space conversion in BOM was 100%, as shown in Table 4, thus indicating the reliability of BOM product inspection. This shows that real and bidirectional configuration results are visualized in real time in collaborative design and that interaction between users is possible. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{
\begin{tabular}{c} **CAD File Total** \\ **Objects** \\ \end{tabular} } & \multicolumn{2}{c}{**Number of Objects**} & \multirow{2}{*}{**Results**} \\  & & **Converted to** & & \\  & & **Address Space** & & \\ \hline
1 & 29 & 29 & \\
2 & 28 & 28 & \\
3 & 3 & 3 & \\
4 & 14 & 14 & 100\% \\
5 & 3 & 3 & \\ \hline Sum & 77 & 77 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Translation accuracy of Address Space in BOM. Figure 16: Experiment diagram. For CAD data, it should be converted to a data format that can be processed by the VR engine. The Unity engine can only handle two types of data: FBX and OBJ. Almost all CAD systems have data preprocessing for generating FBX and OBJ data, but the generated FBX and OBJ do not have the quality to be used directly as VR data. The FBX and OBJ data formats visualize the 3D shape of the object using polygon elements, and the number of polygons used is critical to the 3D graphic quality of the visualization object. The higher the number of polygons, the more realistic is the 3D object and the 3D graphics quality is higher. However, having a large number of polygons increases data volume and leads to performance problems in VR applications. Since this study requires optimization to ensure a balance between graphic data quality and VR application performance, the optimization value is calculated by calculating the capacity size of the extracted FBX file and the existing CAD file using Equation (2) by importing and exporting CAD files from the VR Parser. Figure 18 shows the optimization results. The results of the optimization test comparing the size of the CAD design file, as presented in Table 5, show an average optimization value of 37.4%. We confirmed an improvement of 12.6% over the optimization default of 50%, which can be expected to  
Figure 17: Data conversion of CPS server. Figure 18: Data optimization size. improve the performance of the same VR application and the quality of 3D graphics in the IT infrastructure.