AI for Manufacturing Quality Control: Vision, Defects, and Decisions

Quality inspection has always been a balance between speed, accuracy, and cost. AI, especially modern computer vision, has shifted that balance in ways that are reshaping how factories think about defects. This course gives you a calm, structured introduction to the field without assuming you have a manufacturing or AI background. You will learn what kinds of defects vision systems detect well, where they still struggle, and how decisions flow from inspection to action on the production line. The course stays grounded in widely used concepts and points to the modern advances reshaping smart factories. What you'll learn: - Understand the goals of manufacturing quality control and where AI now fits in - Recognize the main families of computer vision defects including surface, dimensional, and assembly issues - Explore the role of lighting, optics, and camera setup in successful vision inspection - Read the typical workflow from inspection trigger to image capture to decision and action - Compare modern deep learning approaches including classification, segmentation, and anomaly detection - Understand the role of edge inference, line speed constraints, and integration with PLC systems The course begins with the goals of quality control, moves through the AI techniques used in modern inspection, and closes with how decisions become actions on a real production line. Written exercises help you connect each concept to a real product or factory floor. This course is designed for absolute beginners with no manufacturing or AI background, including engineering students, software developers entering industrial work, and operators curious about new inspection tools. No prerequisites are needed. The course explains every concept as it appears and respects the realities of factory operations.