## 4 Implementation and Results
### Implementation of the NGIM HCPS 2.0-Based MES
Figure 17A We then proceeded with the labeling to designate the holes in the original images. Figure 17B  
Figure 16: Log file status by facility. shows a list of data points that were displayed in this way. The images in Figure 17C show the labeling data in the first row, the epoch 25 data in the second row, and the epoch 100 data in the third row. Figure 17D shows the original images, the resulting images, and the resulting images overlaid onto the original images. Figure 18 shows detailed information about the hole images that were measured using the AI method. The table on the left shows the image names, LOT numbers, processing control numbers, and other detailed information about the holes. The four photos on the right show an example of an original image, the corresponding preprocessed image that measured the hole in the original image, the image that was measured by the operator, and the image and measurement values that were measured by the AI method. These images allowed the workers to monitor what the AI method was measuring. Using data visualization, a real-time monitoring program was implemented to view the status of inspection progress by unit, the LOT status for each process, and the progress status for each LOT. Figure 19 depicts the inspection process of the HCPS unit, which calculated and displayed in real time the scheduled inspection time, inspection completion time, and remaining time for each unit inspection, including the first inspection, heavy inspection, and final inspection. This allowed the workers to keep track of whether the inspections were being completed on time. Figure 17: The U-Net training process. Figure 20 shows the LOT status for each process. It was possible to check which LOT was in each progress by monitoring the LOT status of all of the processes, from order receipt to shipment. Figure 19: The inspection process of the HCPS unit. Figure 18: Information about an example hole that was measured using the AI method. Figure 21 shows the progress status of each LOT. The LOTs could be searched according to date in order to monitor the status and current progress of each LOT. Figure 22 shows the log file status for each facility and the log transmission status for each facility, which were implemented by the IoT data management. All of the data for the log file status of each facility and the log transmission status for each facility were expressed in table format so that workers could check the data by date, facility, log file, and LOT. Figure 20: The LOT status for each process. Figure 23 shows the operation/non-operation status and abnormal occurrence management by facility. Both operation/non-operation status and abnormal occurrence management were included so that the operation and non-operation status of all facilities could be checked at the same time to deal with abnormal occurrence management when the causes of non-operation were abnormal. Figure 23: The operation/non-operation and abnormal occurrence management by facility. Figure 22: The log file status for each facility. The proposed service implemented the MES system of the company, which was based on the laser process and collected real-time production results via the facility interface. The management of the real-time manufacturing conditions used the data for the facility PLC linkage, operation time/production performance/manufacturing conditions (time and processing cycles, etc.) for each facility and database, log file analysis, and statistical work. It aimed to improve corporate competitiveness by innovating the processes and quality control using statistical data. To that end, we developed an MES system that could reduce manufacturing lead times, process defect rates, and delivery times, among other KPIs, through the facility operation processes, log file collection and analysis, and data analysis for each facility.