This paper presents the design and implementation of a shadow-type digital twin applied to an automated industrial cell for printed circuit board (PCB) processing. The physical system, briefly described for contextual purposes, consists of an integrated robotic cell with CNC routing stations and multi-axis manipulators responsible for material handling and tray exchange. This setup serves as the real-world asset for validating the proposed digital twin architecture.

The core contribution of this work lies in the development of a digital twin based on the shadowing paradigm, in which operational data, process states, and event-driven information are continuously mirrored from the physical system to a virtual environment in real time. Unlike simulation-oriented or predictive digital twin approaches, the proposed model prioritizes high-fidelity synchronization between the physical asset and its digital counterpart, without exerting control over the production process. This strategy enables accurate process virtualization, state tracking, and historical memory construction while preserving system autonomy and operational safety.

The implemented digital twin provides enhanced capabilities for monitoring, traceability, and performance assessment, supporting data-driven analysis and decision-making at the supervisory level. Experimental observations indicate that the proposed approach improves operational transparency and establishes a scalable foundation for future extensions, including predictive maintenance, process optimization, and deeper integration with Industry 4.0 architectures. The results demonstrate that shadow-type digital twins represent a practical and effective solution for virtualizing complex industrial systems without disrupting existing control structures.