ARTIFICIAL INTELLIGENCE IN QUALITY CONTROL: A REVIEW OF METHODS, APPLICATIONS, AND EMERGING DIRECTIONS
Keywords:
Artificial Intelligence, Quality Control, Deep Learning, Machine Vision, Predictive MaintenanceAbstract
Today, Statistical Process Control (SPC) and manual inspection methods (classical SPC) employed in quality control (QC) can't manage the volume, velocity, and variety of the data generated by production systems. Artificial Intelligence (AI), which encompasses classical machine learning (ML), deep learning (DL), computer vision, generative modeling, and explainable AI (XAI), has become the key factor for automating the quality assurance process in both manufacturing and process industries, making it adaptive and predictive. This review categorizes the application of AI across the lifecycle of the QC process, including the examination of visual defects, prediction of maintenance issues and statistical process control, as well as anomaly detection and document/compliance management. The literature is classified based on the class of the technique and based on industrial sector, that is automotive and general manufacturing, electronics and semiconductors, pharmaceuticals, food processing, construction, with typical benchmark datasets used, performance results, and current issues like lack of data, class imbalance, explanation, and integration. The conclusion of this review states that transformer and convolutional vision models are predominant in defect detection; classical ensemble methods compete well in tabular sensor-based predictive maintenance and SPC; generative models are becoming more popular in tackling the problem of insufficient defects to train models for defect detection, and the need for interpretability is increasing for critical or regulated applications. Future work areas identified are federated and edge learning, human-in-the-loop inspection, and cross-sector benchmarking.
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Copyright (c) 2026 Mohammed Abbas Abdullah , Ammar Jawad Taher , Talib Hussein Abdullah
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