Detecting and analyzing defects in components or systems is crucial for maintaining high-quality standards in modern manufacturing and quality control. Recently, imaging-based defect detection methods have gained popularity across various engineering fields, highlighting their growing importance. Additionally, the integration of Artificial Intelligence (AI) to improve accuracy and efficiency is rapidly advancing. This paper presents a system that uses imaging to detect holes in CV joint boots, as these holes significantly affect the overall performance and durability of the system. Moreover, it introduces a method for enhancing detection performance by applying AI techniques. Validation tests on actual CV joint boots confirmed that the proposed method improves detection performance.
This paper presents a vision system for machined hole quality inspection of mechanical parts in an automobile. Automobile parts have various shapes and holes created by press punches. However, if the press punch pin is broken, a hole is not created on the mechanical parts. This problem causes serious part quality defects. To solve this problem, we proposed a vision system that could easily and cheaply inspect the quality of holes in automotive machining parts. A software development environment was created to build an economical vision inspection system. Images were gathered using the Near-real-time method to overcome the low frame-per-second of inexpensive Complementary Metal Oxide Semiconductor (CMOS) webcams. Status of the hole was determined using template matching and distance between holes as a feature. The hardware required for vision inspection was designed so that it could be directly applied to the automotive part manufacturing process. When the proposed vision inspection system was tested by installing it in an automobile parts factory for 3 months, the system showed an inspection accuracy of at least 97.9%. This demonstrates the effectiveness of the proposed method with accuracy and speed of hole defect inspection of machined parts.