A Novel Digital Twin Driven Multi-Camera Edge Computing Sensing System for Additive Manufacturing

Abstract

Ensuring real-time quality assessment for Additive Manufacturing (AM) is challenging because of the sequential layer-by-layer deposition process. This paper presents a novel digital twin-driven multi-camera edge computing sensing system designed for real-time dimension monitoring in AM, offering 360° coverage of the build area. Four calibrated cameras positioned orthogonally were synchronized and processed on an NVIDIA based edge device to enable the 3D reconstruction of the final AM product. Stereo calibration was performed into two camera groups, yielding reprojection errors of 1.19 pixels and 1.34 pixels respectively. The resulting 3D measurements closely matched the expected physical dimensions, demonstrating reliable spatial consistency across both the groups. The reconstructed geometry was integrated into a Digital Twin environment, enabling real-time visualization and dimensional verification. This system demonstrates the feasibility of synchronized multi-camera setups with geometry aware quality assessment, achieving a consistently low mean error. The proposed system is the foundation of Digital Twin-driven real-time process monitoring and feedback control in AM