ROBOT VISION FOR SMART QUALITY CONTROL IN INDUSTRY 4.0 – INDUSTRIAL CASE STUDY

Abstract

Modern manufacturing requires more advanced approaches to quality control due to the limitations of traditional methods, which are inefficient, subject to human error, and insufficiently flexible for the growing demands of customers for personalized products. Complexity, along with the inability of traditional control to accurately identify errors and irregularities in the production process, imposes the need for the application of contemporary Industry 4.0 tools. The rapid development of Industry 4.0 technologies has transformed quality control systems by integrating smart robotics, advanced machine vision, and data-driven decision-making. Smart robotic systems enable real-time inspection, adaptive defect detection, and high levels of process automation, thereby reducing human error and increasing production efficiency. This paper investigates the role of intelligent robotic platforms in improving quality assurance through the application of autonomous visual inspection and AIenhanced analytics. A case studies demonstrates the implementation of a smart robotic inspection workstation designed to optimize cycle time, increase detection accuracy, and support continuous process monitoring. The results indicate significant improvements in defect identification performance, operational consistency, and production traceability. The findings highlight the potential of smart robotic systems as a key enabler of digital transformation and a foundational component of next-generation quality control in Industry 4.0.