Quality assurance in modern production environments is becoming increasingly complex due to automation, high production speed, and the need for near-zero defects. Traditional quality control methods rely heavily on manual inspection and statistical sampling, which often fail to detect hidden patterns or early indicators of defects. Artificial Intelligence (AI) and Machine Learning (ML) technologies provide new opportunities for improving production quality through predictive analysis, automated inspection, and real-time decision making. This study explores the application of AI and ML tools in implementing effective quality assurance mechanisms in manufacturing systems. The research proposes a framework where machine learning algorithms analyze production data to monitor and evaluate quality metrics such as defect rate, process capability, and production consistency. The methodology includes data collection from simulated production systems, model training for anomaly detection, and evaluation using standard quality indicators. Results indicate that AI-enabled quality assurance systems significantly enhance defect detection accuracy and reduce manual intervention. The findings highlight the potential of AI tools to support continuous improvement in manufacturing processes while ensuring consistent product quality. The study concludes that integrating AI with production quality metrics can transform traditional quality management practices into intelligent and adaptive systems.
Tausif Magbul Mujawar (Sat,) studied this question.