Towards Intelligent Manufacturing: Deep Learning Approaches for Automated Visual Inspection and Fault Diagnosis

Recent breakthroughs in artificial intelligence have accelerated the adoption of learning algorithms, visual data categorization, and defect identification methods within quality control workflows across manufacturing industries. This review thoroughly explores the incorporation of AI-based solutions within industrial workflows, emphasizing the improved performance of neural network-driven techniques as opposed to classical inspection strategies. Specifically, CNN architectures (convolutional neural networks) and advanced YOLO models (YOLOv5, YOLOv7, YOLOv8) have exhibited outstanding precision and speed in detecting structural anomalies and identifying faults as they occur. By utilizing attention mechanisms, architectural refinements, knowledge transfer techniques and synthetic data expansion, these models have demonstrated enhanced capabilities—particularly in cases where training data is scarce. Transformer-based and hybrid neural network designs have shown great potential as effective alternatives, ensuring a balance between accuracy and computational demand. Research findings suggest that AI-based quality control frameworks play an essential role in minimizing manufacturing defects, elevating product quality, and improving overall customer satisfaction. These advancements span multiple industries, including automotive, steel, textiles, electronics, and more. They strongly align with Industry 4.0 goals, enabling the shift towards digitalized manufacturing systems, thereby fostering intelligent and automated production environments.