How Deep Learning is Implemented in Manufacturing Quality Control

This study aims to explore the application and effect of deep learning technology in manufacturing quality control. The study deployed a convolutional neural network (CNN) model to analyze images and sensor data during the manufacturing process to achieve automatic detection and classification of defects. The experiments were conducted in a simulated high-speed production line environment, and a large amount of production data, including images and sensor readings, were collected and processed. The results showed that the accuracy of the deep learning model in identifying serious, moderate and minor defects reached 98%, 95% and 90% respectively, which was significantly higher than the traditional quality control system. The conclusion shows that deep learning technology can significantly improve the quality control accuracy and efficiency of the manufacturing industry, provide strong technical support for the field of intelligent manufacturing, and indicate broad application prospects in advanced manufacturing systems that require higher levels of automation and intelligence.