This paper describes an AI-based method for detecting and measuring cracks in the lower mold of a press. During continuous press operation, cracks can form in the lower mold and lead to product defects, which makes manual inspection by operators necessary. In this study, images of the lower mold were captured using a mechanical system developed in our previous study, in which an endoscopic camera is installed at the center of the upper mold. An AI program was then developed using YOLOv8. Images of cracked lower molds were captured at various camera heights, and the crack regions in the captured images were annotated with bounding boxes to train the AI model. Using the trained model, the AI program determines the presence of cracks in the lower mold and calculates the actual size represented by each pixel according to the camera height, thereby enabling the crack dimensions to be measured. The developed program can reduce the burden of manual inspection and provide quantitative information for determining maintenance schedules, thus contributing to the prevention of product defects and the stable operation of press processes.
This paper describes the calibration of a 2D vision system utilizing a high-precision robot that moves accurately along the z-axis. To achieve this, a one-axis high-precision robot was designed and manufactured, achieving a minimum travel distance of 0.007 μm during testing. The 2D vision system comprised an RGB camera, an LVDT sensor, and a laser sensor for distance measurement. Additionally, a subpixel-based algorithm was developed for the calibration and size measurement of the system. Following the calibration process with the high-precision robot, the size error was found to be within ±0.1 mm when using the LVDT sensor and ±0.2 mm with the laser sensor. Thus, the 2D vision system and calibration algorithm presented in this paper are deemed suitable for accurate object measurement by robots.
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