INTELLIGENT QUALITY INSPECTION IN SMART MANUFACTURING USING COMPUTER VISION AND ARTIFICIAL INTELLIGENCE

Abstract

The rapid advancement of Industry 4.0 technologies has significantly transformed modern manufacturing processes by integrating automation, artificial intelligence (AI), and datadriven decision-making. Quality inspection remains a critical aspect of manufacturing, as product defects can lead to increased production costs, customer dissatisfaction, and reduced operational efficiency. Traditional inspection methods often rely on manual observation or rule-based systems, which are time-consuming, prone to human error, and difficult to scale in high-volume production environments. To address these challenges, this study presents an intelligent quality inspection system for smart manufacturing using computer vision and artificial intelligence. The proposed system employs advanced image acquisition techniques and AI-based image processing algorithms to automatically detect, classify, and evaluate product defects in real time. High-resolution cameras capture images of manufactured components, while computer vision techniques perform feature extraction and defect localization. Deep learning models, particularly convolutional neural networks (CNNs), are utilized to identify surface defects, dimensional inconsistencies, and manufacturing anomalies with high accuracy. The system is integrated into a smart manufacturing environment, enabling continuous monitoring and automated quality assessment without interrupting production workflows. Experimental results demonstrate that the proposed approach achieves superior defect detection accuracy, reduced inspection time, and improved consistency compared with conventional inspection methods. The AI-driven inspection framework effectively minimizes false detections while enhancing overall production quality and operational efficiency. Furthermore, the system supports predictive quality management by providing valuable insights for process optimization and defect prevention. The study highlights the potential of combining computer vision and artificial intelligence to develop reliable, scalable, and intelligent quality inspection solutions for next-generation smart manufacturing systems.