Data Collection for Training Quality-Control AI in Carpet Manufacturing
Researchers have proposed a machine-vision system designed to improve quality control in carpet manufacturing. The system aims to inspect carpet webs in real-time and systematically collect labeled images of defects to train future quality-control AI models. This approach is grounded in a Six Sigma project at a carpet production facility, addressing a potential bottleneck and high defect rates. AI
IMPACT This system could significantly improve efficiency and consistency in industrial quality control processes by automating defect detection and enabling continuous model improvement.