Aluminum casting quality control using computer vision techniques
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.
Quality control automation using computer vision: A case study of bottle caps
Quality control (QC) in the packaging industry, especially in bottle cap production, encounters some challenges in detecting defects such as loosely fitted caps, scratched parts, or broken cap rings. Today artificial intelligence (AI), computer vision (CV) in particular, can be used to effectively automate this procedure in real-time and minimize human errors.