Research Review on AI-Enhanced Machine Vision and Robotics for Predictive Maintenance & Quality Inspection in Automated Manufacturing
TL;DR
This paper systematically sorts out technical routes, system architectures and engineering practices of the two major application scenarios, and discusses cutting-edge directions for collaborative integration of vision and robotic technologies, providing systematic references for engineering implementation and relevant research layout.
Abstract
With automated manufacturing advancing toward high precision and high efficiency, the integration of artificial intelligence, machine vision and industrial robots has become the core pillar of intelligent manufacturing. Traditional manual inspection and scheduled maintenance suffer from high missing detection rates, delayed response and excessive operating costs, failing to satisfy strict quality control standards of modern manufacturing. Deep learning empowers machine vision to extract defect features and realize automatic classification efficiently, while industrial robots break free from fixed programming to achieve autonomous perception and decision-making. Supported by multi-sensor data fusion and machine learning, predictive maintenance enables early equipment fault identification and drastically cuts unplanned downtime. Vision-guided robotic inspection systems outperform human operators in both detection speed and precision. This paper systematically sorts out technical routes, system architectures and engineering practices of the two major application scenarios, and discusses cutting-edge directions for collaborative integration of vision and robotic technologies, providing systematic references for engineering implementation and relevant research layout.