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Visualisation of in-process 3D deviation mapping and defect monitoring in high production-rate robotic additive manufacturing

Commonwealth Scientific and Industrial Research Organisation
Vargas Uscategui, Alejandro ; Gautam, Subash ; King, Peter ; Lohr, Hans ; Bab-Hadiashar, Alireza ; Cole, Ivan ; Asadi, Ehsan
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25919/a97q-tx79&rft.title=Visualisation of in-process 3D deviation mapping and defect monitoring in high production-rate robotic additive manufacturing&rft.identifier=https://doi.org/10.25919/a97q-tx79&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=Additive manufacturing (AM) is an emerging digital manufacturing technology for producing complex and freeform objects through layer-wise deposition. High-deposition-rate robotic AM (HDRRAM) processes, such as cold-spray additive manufacturing (CSAM), offer significantly higher build speeds by delivering large volumes of material per unit time. However, maintaining shape accuracy remains a critical challenge, particularly due to process instabilities in current open-loop systems. Detecting these deviations as they occur is essential to prevent error propagation, ensure part quality, and minimise post-processing requirements. This video presents a real-time monitoring system that acquires and reconstructs the growing part and directly compares it with a near-net reference model to detect shape deviations during the manufacturing process. The early identification of shape inconsistencies, followed by the segmentation and tracking of each deviation region, paves the way for timely intervention and compensation to achieve consistent part quality.Lineage: Video was produced using data collected from live video feeds and surface 3D-reconstructed data from sensors and robotic systems via proprietary algorithms and software, thereby creating a digital twin of a manufacturing process. &rft.creator=Vargas Uscategui, Alejandro &rft.creator=Gautam, Subash &rft.creator=King, Peter &rft.creator=Lohr, Hans &rft.creator=Bab-Hadiashar, Alireza &rft.creator=Cole, Ivan &rft.creator=Asadi, Ehsan &rft.date=2026&rft.edition=v1&rft_rights=Creative Commons Attribution Noncommercial-Share Alike 4.0 Licence https://creativecommons.org/licenses/by-nc-sa/4.0/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO 2026.&rft_subject=In-process monitoring&rft_subject=geometric defect detection&rft_subject=deviation mapping&rft_subject=2D laser profiler&rft_subject=Cold spray additive manufacturing (CSAM)&rft_subject=High-rate additive manufacturing&rft_subject=Automation engineering&rft_subject=Control engineering, mechatronics and robotics&rft_subject=ENGINEERING&rft_subject=Control engineering, mechatronics and robotics not elsewhere classified&rft_subject=Additive manufacturing&rft_subject=Manufacturing engineering&rft_subject=Manufacturing engineering not elsewhere classified&rft_subject=Materials engineering not elsewhere classified&rft_subject=Materials engineering&rft.type=dataset&rft.language=English Access the data
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https://creativecommons.org/licenses/by-nc-sa/4.0/

Data is accessible online and may be reused in accordance with licence conditions

All Rights (including copyright) CSIRO 2026.

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Additive manufacturing (AM) is an emerging digital manufacturing technology for producing complex and freeform objects through layer-wise deposition. High-deposition-rate robotic AM (HDRRAM) processes, such as cold-spray additive manufacturing (CSAM), offer significantly higher build speeds by delivering large volumes of material per unit time. However, maintaining shape accuracy remains a critical challenge, particularly due to process instabilities in current open-loop systems. Detecting these deviations as they occur is essential to prevent error propagation, ensure part quality, and minimise post-processing requirements. This video presents a real-time monitoring system that acquires and reconstructs the growing part and directly compares it with a near-net reference model to detect shape deviations during the manufacturing process. The early identification of shape inconsistencies, followed by the segmentation and tracking of each deviation region, paves the way for timely intervention and compensation to achieve consistent part quality.
Lineage: Video was produced using data collected from live video feeds and surface 3D-reconstructed data from sensors and robotic systems via proprietary algorithms and software, thereby creating a digital twin of a manufacturing process.

Available: 2026-01-16

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2D laser profiler | Additive Manufacturing | Automation Engineering | Cold spray additive manufacturing (CSAM) | Control Engineering, Mechatronics and Robotics | Control Engineering, Mechatronics and Robotics Not Elsewhere Classified | Engineering | High-rate additive manufacturing | In-process monitoring | Manufacturing Engineering | Manufacturing Engineering Not Elsewhere Classified | Materials Engineering | Materials Engineering Not Elsewhere Classified | deviation mapping | geometric defect detection |

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