Intelligent Vision-Based Inspection Techniques for Modern Manufacturing

Abstract

As artificial intelligence becomes increasingly integrated into vision-based industrial automation equipment, a new generation of inspection systems has emerged, enabling faster and more accurate quality control to be performed simultaneously with the manufacturing process. The current study highlights the use of state-of-the-art sensors, artificial intelligence, robotic manipulators, and Internet of Things-based data mining to identify imperfections and measure dimensions across different production sectors. The use of machine vision, high-precision lenses, and defect detection/classification deep learning modules leads to fully automatized quality checking with minimal human involvement. In addition, by integrating advanced manufacturing and operations techniques like 3D scanning, edge computing, and predictive maintenance, the system efficiency is further enhanced. The sectors of electronics, automotive, and precision production products were chosen for demonstration purposes to illustrate how these methods improved the time for one control step, the percentage of defects found, as well as total throughput. The findings are evidence of this technology’s capacity not only to assist with quality control but also to function as an important component of smart manufacturing and Fourth Industrial Revolution plans.