Digital Twin-Assisted Computer Vision Framework for Industry 5.0 Smart Manufacturing Applications

Abstract

The industry 5.0 scenario of manufacturing requires intelligent, adaptive and human-centric production systems that are able to realize high precision, reduced downtime and real-time decision making. But the traditional manufacturing systems have significant problems like failure of equipment, inefficient monitoring, quality inconsistencies and limited predictive maintenance. To overcome these problems, this paper introduces Digital Twin-assisted Computer Vision Framework suitable for smart manufacturing applications. The framework combines real-time digital twin capabilities with cutting-edge computer vision and image processing algorithms using AI to establish a virtual representation of industrial processes that can be continuously monitored and optimized. The proposed system uses deep learning methods such as Convolutional Neural Networks (CNN), object detection algorithms, and semantic image segmentation to detect defects, analyze interaction between worker and machine, and monitor production line. Experimental results show better manufacturing accuracy, shorter time to detect faults, better efficiency of predictive maintenance and greater operational productivity when compared to traditional monitoring systems. The proposed framework presents a great future industry potential for autonomous manufacturing, intelligent robotics, adaptive quality control and implementation of smart factories (Industry 5.0) based on sustainability.