## 4 Implementation and Results
### Implementation of the NGIM HCPS 2.0-Based MES  
The facility operation rate improved, as did worker awareness, by using the real-time facility allocation status analysis. The goal was to increase the facility uptime by preparing LOT and minimizing facility downtime. Figure 14 shows the status of the facility operation. Figure 13: A flow chart for each major process. The qualitative target of the process defect rate was measured once a quarter and surveys and usage status interviews were conducted. This aimed to improve satisfaction by generating and improving improvement items. The quantitative performance analysis was executed once a quarter and the targets were managed by providing comparative data on the quantitative performance before and after implementation in each quarter. Figure 15 shows the status of a process inspection. There was an OPC agent as a component of the IoT platform, which was installed in the processing machine. It detected changes in the log files that were targeted by the processing machine and when a change was detected, the relevant file was sent to the server. Because the machine's hardware requirements were low, it tried to use as few resources as possible. The essential requirements of the agent were Windows XP, Windows 7 or Windows 10,  
Figure 14: The status of the facility operation. Figure 15: The status of a process inspection. Java 7 or Java 8, at least 32 MB (but recommended 128 MB) of memory, and a network environment that could communicate with the server. The agent status and transmitted log files were collected and stored on a disk by the OPC server. Figure 16 depicts the log file status for each facility, which were gathered in this manner. The OPC agent compared the collected files to those in the database and saved the differences. Tomcat 8.5, Java 8, Microsoft SQL Server 2019, and a network environment that was capable of communicating with the agent were all required for this process. In this study, we tried a traditional image processing method to detect holes in images. At first, when a circle was detected using the existing circle detection algorithm, the correct circle could not be detected when there was a lot of noise in the image. In addition, after applying preprocessing technology that separated the color and texture of the area in which the circle should be detected, an attempt was made to detect approximate circles using the RANSAC algorithm, but the boundary of the circles was not clear. The reasons that it was difficult to apply these traditional image processing techniques were as follows. 1. The PCB board was processed using a laser, so there were numerous heat-scorched marks around the holes; therefore, in order to detect accurate circles by excluding all of these numerous instances of noise, it was judged that the traditional algorithms for detecting images that conformed to specific ruled were not suitable;
2. The images of holes were divided into two types: TOP, in which the boundaries of the holes were clear enough to be identified with the naked eye; and BOTTOM, in which the boundaries of the holes were not easily distinguished, even by the naked eye;
3. Only the size of the hole was measured to determine whether or not it was acceptable; in the future, the goal would be to detect various types of defects, such images that contain foreign substances, images that are tilted to one side or over-processed images. For this reason, we wanted to measure the hole sizes using the AI method. Therefore, in this study, our microscopic image analysis that was based on deep learning vision technology for real-time quality inspection used U-Net, which is a deep learning algorithm that is effective in the image segmentation of medical images. Figure 17 shows the U-Net training process. The contour detection algorithm detected an area in the image and measured the diameter after masking in the hole image. A total of 144 data points were used: 100 training data points, 28 validation data points, and 16 test data points.