Design of an Intelligent Manufacturing System for Production Workshop Parts Departments Based on Deep Learning

Junyong Li, Boge Yu, Weiqing Cai, Qiongfang Gui

In the wave of intelligent manufacturing transformation, production workshops are facing core challenges such as relying on manual quality inspection, lagging equipment failure prediction, and rigid production scheduling. Deep learning technology, with its powerful perception and decision-making capabilities, provides a new way to solve these bottlenecks. This research has designed a workshop level intelligent manufacturing system architecture integrating edge computing and cloud intelligence. Focusing on three major directions of part quality control, equipment health management and production scheduling, it has optimized and implemented core algorithms including real-time high-precision defect detection based on improved You Only Look Once version 7(YOOv7), residual life prediction using a dual stream Transformer architecture, and adaptive dynamic scheduling based on deep reinforcement learning. Experiments and system simulations have shown that the system has significantly improved key performance indicators such as real-time detection, prediction accuracy, and scheduling optimization. This solution provides a reliable technical path and a complete system level solution for realizing a closed-loop intelligent production system of “perception decision execution”, and has clear engineering application value.