ROBOTIC INSPECTION SYSTEMS IN DATA-DRIVEN MANUFACTURING: ARCHITECTURES, APPLICATIONS, AND IMPLEMENTATION INSIGHTS FROM ROMANIAN INDUSTRY

David CHERECHES, Alexandru MOLDOVAN, Mircea FULEA, Mircea MURAR, Bogdan MOCAN

Abstract


This paper presents an analysis of contemporary robotic automation systems deployed in industrial quality control processes, with specific applications in Romanian manufacturing sectors. The research examines the integration of advanced sensing technologies, machine learning algorithms, and adaptive robotic systems to enhance measurement accuracy, consistency, and throughput in manufacturing environments. Through four case studies involving cast parts, electrical wires, wood components, and mold parts from manufacturing industries within Romania, we demonstrate up to a 99% improvement in defect detection and up to a 68% reduction in inspection times compared to traditional methods. The implementation framework proposed addresses common challenges in system calibration, environmental variability, and cross-platform data integration within the context of Romania's transition toward Industry 4.0 standards. Our findings suggest that the convergence of robotics with Industrial Internet of Things (IIoT) platforms represents a critical advancement in quality assurance for Romania's evolving manufacturing ecosystems.


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