AI-Driven Real-Time Gear Inspection: A Computer Vision-Based Approach for Precision Manufacturing

Abstract

Gear inspection is crucial for ensuring precision in manufacturing, yet traditional sampling methods are time-consuming and prone to errors. This paper introduces an AI-driven, computer vision-based system for real-time, non-contact gear inspection. The system uses a conveyor belt, a high-resolution camera, and machine learning algorithms to analyze gear parameters against stored reference data. Unlike conventional methods, it integrates deep learning for defect detection, hyperspectral imaging for material consistency, and 3D laser scanning for precise measurement of involute profiles. Edge AI processing enables real-time analysis, reducing latency, while a precision pneumatic system efficiently rejects defective gears. The system enhances accuracy, minimizes human intervention, and improves scalability for high-speed production lines. Future enhancements include predictive maintenance using AI and blockchain-based quality tracking. This research demonstrates that AI-powered vision systems can revolutionize gear inspection, ensuring superior quality control in manufacturing industries.