A computer vision as a tool for automated quality control in smart manufacturing

Abstract

Computer vision (CV) has emerged as one of the most significant enablers of intelligent factoring system quality control, automated in the context of the AI revolution in the industrial setting today. In this research, we discuss how CV-based architecture can be applied to achieve real-time, adaptive, and scalable quality assurance. This is new research because it is an amalgamation – the evaluation of different mathematical models and artificial intelligence (AI). Deep learning, transfer learning, Bayesian networks, and edge computing are among the solutions, as are fog-cloud partnerships and their direct impact on manufacturing output, productivity, and decision-making efficiency. The article provides comparative data on the performance of other CV frameworks in different industrial conditions by critically examining the new case studies. The practical implications are recommendations for adopting vision-driven systems to improve product consistency, increase human-machine interaction, and reduce operational downtime. In addition, the paper identifies shortcomings in computational resources, system compatibility, and information security that should be addressed in the next generation of smart factories.