In the context of accelerating digitalization, reliable object reconstruction represents a key prerequisite for developing accurate and functional digital twins. This study introduces a unified evaluation methodology designed to assess and compare optical 3D scanning technologies in terms of geometric accuracy, data completeness, and model consistency. The framework integrates all essential stages of digital reconstruction—from data acquisition to quantitative validation—ensuring reproducibility and comparability of results across different optical systems. To verify its applicability, two optical principles, photogrammetry and structured-light scanning, were implemented on the autonomous mobile robot MiR100. The reference CAD model in a 1:1 scale served as the ground-truth geometry for all analyses. Evaluation procedures included visual inspection, dimensional measurements, and statistical error analysis performed in MeshLab, CloudCompare, and MATLAB. The results confirmed that photogrammetry provides high-quality textural detail but suffers from geometric noise and scale drift (relative error > 10%), whereas structured-light scanning delivers more stable and metrically accurate results. In particular, the scanner mode achieved the highest precision, with a mean deviation of 17.4 mm, RMSE of 26.8 mm, and relative error of 7.6%. The proposed methodological framework thus establishes a reproducible basis for evaluating 3D reconstruction accuracy and supports the integration of optimized digital models into digital twin environments.

This study investigates the real-time monitoring of 3D printing using an endoscopic camera system integrated directly into the print head. The embedded endoscope enables continuous observation of the area surrounding the extruder, facilitating real-time inspection of the currently printed layers. A convolutional neural network (CNN) is employed to analyse captured images in the direction of print progression, enabling the detection of common defects such as stringing, layer shifting, and inadequate first-layer adhesion. The primary innovation of this work lies in its capacity for online quality assessment and immediate classification of print integrity within predefined thresholds. This system allows for the prompt termination of printing in the case of critical faults or dynamic adjustment of printing parameters in response to minor anomalies. The proposed solution offers a novel pathway for optimising additive manufacturing through real-time feedback on layer formation.

This study proposes a unified, technically reproducible methodology for transforming real industrial systems into interactive digital twins deployable across extended reality (XR) environments. The approach integrates all stages required to convert physical equipment into a performant virtual representation: selection of acquisition methods; creation of 3D models using CAD modeling, photogrammetry, structuredlight scanning, and laser scanning; geometric cleaning and polygon optimization; preparation of the virtual environment in Unreal Engine; model optimization using tessellation and Nanite; and implementation of realtime data connectivity. The methodology employs the Asset Administration Shell (AAS) standard and OPC UA protocol to synchronize the virtual model with operational data from physical devices, enabling dynamic updates of system states. Interaction logic is implemented through Blueprint scripting, allowing the digital twin to visualize parameters, monitor device behavior, and execute control functions within XR. Experimental evaluation shows that geometric optimization combined with Nanite-based rendering increases runtime performance by up to 167% for scanned models compared to tessellation-based rendering, while maintaining stable real-time visualization. The results demonstrate that heterogeneous 3D models can be successfully optimized for XR constraints and integrated with real industrial signals, forming fully functional digital twins. The methodology provides a scalable foundation for deploying XR-based monitoring, training, and control applications, highlighting its relevance for Industry 5.0 systems that require seamless humanmachine interaction.