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

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 (HDR-RAM) 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 study proposes a novel geometric digital twin (gDT) framework, gDT-AM, specifically designed for HDR-RAM. It continuously captures and maps the part’s geometry in real-time during printing. It incorporates two experimentally validated alternative methods for precise, fast surface reconstruction from sparse spatio-temporal 2D laser profiler scans. This supports the monitoring of geometric anomalies during deposition by providing timely, actionable information for intervention to reduce material waste and process inconsistency. The proposed framework provides a basis for online process-parameter optimization and toolpath correction. Ultimately, integration of the digital twin aligns with the principles of intelligent manufacturing through connectivity, automation, data-driven decision-making, and adaptive process control.