Automated Bolt Surface Defect Detection and Cloud-Based Quality Monitoring
Since traditional quality inspection methods rely heavily on human observation, they suffer from limitations in consistency and reliability. As production volume increases, these limitations introduce higher quality risks. Therefore, the demand for automated, traceable and sustainable inspection systems in manufacturing facilities continues to grow.
Within the scope of this study, a deep learning–based inspection system was developed to automatically detect surface defects on bolts. The system is built on an unsupervised learning structure that operates without the need for defective training samples and it aims to store the detected defects within a cloud-based infrastructure.