ARTIFICIAL INTELLIGENCE-DRIVEN QUALITY CONTROL IN MODERN MANUFACTURING SYSTEMS
Quality control is a fundamental function of manufacturing because product conformity, process stability, customer satisfaction and operational efficiency depend on the ability of manufacturers to detect and prevent deviations from specified requirements. Conventional quality-control practices, although effective in many applications, frequently depend on manual inspection, statistical process control and rule-based decision-making, which may be inadequate for highly customised, high-speed and data-intensive production environments. The emergence of artificial intelligence (AI), machine learning (ML), deep learning (DL), computer vision, industrial Internet of Things (IIoT), edge computing and digital twins has created new opportunities for transforming quality control from a predominantly reactive activity into a predictive and autonomous manufacturing function. This paper examines the application of AI-driven quality control in modern manufacturing systems through a critical review of recent literature. The paper discusses AI-enabled visual inspection, predictive quality, process monitoring, anomaly detection, defect classification, intelligent metrology, predictive maintenance and closed-loop quality control. Particular attention is given to the integration of AI with Industry 4.0 technologies and the implications for production efficiency, waste reduction, process capability and decision-making. The paper also identifies major challenges, including data scarcity, model explainability, algorithmic generalisation, cybersecurity, computational requirements, system integration and workforce readiness. A conceptual framework for AI-driven quality control is proposed, linking data acquisition, intelligent analytics, quality prediction, decision support and corrective action. The paper concludes that AI should not be viewed simply as a replacement for human inspection but as an intelligent layer that augments engineering knowledge and enables manufacturing systems to anticipate, diagnose and correct quality problems. Future research should emphasise explainable, robust, resource-efficient and human-centred AI systems capable of operating reliably in real industrial environments.