Defect Detection in Bearings Using Machine Learning and Computer Vision

In the bearing manufacturing industry, detecting defects in bearings is essential for maintaining product quality and operational efficiency. Traditional manual inspection methods are labor-intensive, costly, and prone to human error. This research paper presents an automated system using machine learning and OpenCV to identify black spots on bearing faces, aiming to replace manual inspection with a more efficient and dependable process. The proposed system effectively reduces inspection time and costs while maintaining consistency. Future work will focus on integrating the system into a conveyor line with a robotic arm for real-time defect removal. This research paper contributes to the field of automated quality control in manufacturing, offering a cost-effective and robust alternative to manual inspection methods.