Efficient detection and rectification of metal components conditions during manufacturing and post-processing manufacturing are crucial for quality control in industries. This paper describes a lab-scale integrated system for real-time and auto-mated metal edge image detection using YOLOv5 machine vision algorithm for automated met-al grinding and chamfering in manufacturing. The YOLOv5 algorithm was compared with VGG-16 and ResNet algorithm for edge detection i.e., sharp edge, chamfer edge, and burrs edge on the metal workpiece. The YOLOv5 algorithm and model were developed and embedded in the NVIDIA Jetson Nano microprocessor. An integrated system connects the NVIDIA Jetson Nano microprocessor with an embedded deep learning image processing model to a Mitsubishi Electric Melfa RV-2F-1D1-S15 robot manipulator to perform the lab-scale manufacturing process for automated grinding and chamfering. The models demonstrates durable performance in detecting the metal edge image for intelligent manufacturing application, achieving a mean average precision 0.854 for ResNet, 0.942 for VGG-16 and 0.957 for YOLOv5, all models across defect classes with minimal misclassifications. The Mitsubishi Electric Melfa RV-2F-1D1-S15 robot manipulator received input from the machine vision system and per-formed an automated grinding and chamfering process accordingly; By integrating camera, embedded deep learning in the microprocessor and robot manipulator, auto-mated grinding and chamfering process in metal edge component can be efficiently rectified. This machine vision technology tailored solution promises to improve productivity and consistency in metal component manufacturing.