Visualisation of geometric digital twin for additive manufacturing (gDT-AM): In-process part surface reconstruction and shape monitoring

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Additive manufacturing (AM) faces critical challenges, particularly in achieving real-time quality control and precision. These challenges are heightened in complex geometries and high-deposition-rate robotic AM (HDRRAM) processes such as robotic Cold-Spray. Traditional quality control methods generally detect defects after the entire part is produced, leading to material waste and increased lead time and cost. The lack of real-time insight during production limits the ability to identify and correct the process variations as they occur. To address this, digital twin technology has emerged as a powerful tool within the intelligent manufacturing paradigm. This video shows a novel geometric digital twin framework, gDT-AM, specifically designed for HDRRAM. It continuously captures and maps the part’s geometry in real-time during printing. It incorporates two experimentally validated alternative methods for precise and fast surface reconstruction from sparse spatio-temporal 2D line profiler scans. Additionally, the gDT-AM introduces an automated layer-by-layer geometric deviation measurement technique. This enables the identification of potential defects during production, facilitating timely interventions that reduce waste and ensure quality. The proposed gDT-AM allows further research on optimising online process parameters and tool-path correction. Ultimately, integrating digital twins embodies the foundational principles of smart manufacturing by facilitating a closed-loop, self-optimising production environment characterised by connectivity, automation, data-driven decision-making, and adaptive process control.
Lineage: Video was produced using data collected from live video feeds and surface 3D-reconstructed data from sensors and robotic systems, using proprietary algorithms and software to create a digital twin of a manufacturing process.