Smart Manufacturing: Real-Time Quality Control with AI and Image Recognition

The integration of Artificial Intelligence (AI) and image recognition technologies into manufacturing processes is revolutionizing traditional quality control systems. This paper explores the development and implementation of a real-time quality control framework using AI-driven image recognition techniques within the smart manufacturing paradigm. The objective is to enhance precision, reduce inspection time, and minimize human error by leveraging machine learning algorithms and computer vision tools. In the proposed system, high-resolution cameras are deployed on production lines to continuously capture visual data of products. These images are analysed using deep learning models trained to detect surface defects, dimensional inaccuracies, and assembly flaws. Unlike conventional inspection methods, which are often manual, time-consuming, and prone to inconsistency, the AI-based solution enables consistent, accurate, and instantaneous decision-making. Furthermore, the study examines the integration of Internet of Things (IoT) devices and edge computing to process and evaluate image data locally, thereby reducing latency and improving response time. The system is designed to learn from ongoing operations, adapt to new defect patterns, and evolve with changing production requirements, thus supporting continuous improvement and predictive maintenance. Experimental results from pilot implementation in an automated assembly line demonstrate a significant increase in defect detection accuracy and a reduction in inspection time. The findings suggest that real-time image-based quality control systems can play a critical role in achieving higher production efficiency, improved product quality, and cost-effective operations in Industry 4.0 environments.