Detecting and classifying aesthetic defects in different sizes, shapes, and positions immediately after the casting process is an essential task for the quality control unit. In this paper, we introduce a simple, low-cost, and efficient optical system powered by deep learning models to quickly, accurately, and automatically identify and classify casting defects.
Automated X-ray systems are improving quality through multiple objective inspections, and reducing labor costs, and increasing productivity and consistency by revising involved processes. This paper proposes a novel approach to train a YOLO-based model for detecting defects in aluminum castings. Deep learning-based models are garnering more attention in the aluminum castings industry every day, and are projected to be even more popular in the coming years due to their excellent effectiveness.