AI-Powered Quality Control and Inspection Systems in Modern Manufacturing

Quality control (QC) is critical in modern manufacturing to ensure product reliability, reduce defects, and enhance customer satisfaction. Traditional QC methods rely heavily on manual inspection, which is time-consuming, error-prone, and inconsistent. The integration of Artificial Intelligence (AI) with advanced sensing and imaging technologies has enabled automated quality control and inspection systems that operate in real time. This paper reviews AI-driven approaches for industrial quality inspection, including computer vision, machine learning, deep learning, and robotics. The updated methodology section explains how QC datasets are collected, annotated, and analyzed using AI models. The architecture of AI-powered QC systems, data pipelines, and defect detection algorithms is presented with figure placeholders. Enhanced case studies provide quantitative improvements in defect accuracy and throughput across automotive, electronics, and food sectors. Challenges such as data quality, system integration, interpretability, and maintenance are critically evaluated. The study concludes that AI-based QC systems are essential for Industry 4.0 manufacturing and will shape the future of smart factories.