The paper presents the design, implementation, and experimental verification of a hybrid camera–laser sensor (Keyence IX‑080) integrated into an automated turbocharger assembly line. The study addresses the limitations of traditional optical and inductive sensors when detecting small metallic components with high reflectivity, variable orientation, and sensitivity to changing lighting conditions. A systematic methodology was developed, including sensor placement design, configuration of image‑ and height‑based detection tools, and integration with the PLC control system. Long‑term testing under real production conditions was conducted over 18,000 assembly cycles, covering natural variability in lighting, handling, and surface reflections. The implemented system achieved a detection success rate of 99.89%, with a Wilson confidence interval of 99.80–99.94%, and demonstrated 100% Poka‑Yoke performance for deliberately introduced errors. The results confirm the high reliability, robustness, and suitability of the hybrid sensor for automated inspection of critical components in turbocharger manufacturing. The findings also highlight opportunities for further optimization of detection tools to increase resilience against reflections and geometric deviations. The study demonstrates that intelligent sensing technologies significantly enhance process stability, product quality, and the overall effectiveness of Smart Factory systems.
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