实时监测高增速机器人增材制造中的形状偏差，提升零件精度
In-process 3D Deviation Mapping and Defect Monitoring (3D-DM2) in High Production-rate Robotic Additive Manufacturing
- 通过实时重建零件并比对参考模型检测偏差
- 可识别并追踪每个偏差区域，防止误差累积
- 适合高速增材制造中质量控制与在线补偿
增材制造（AM）是一种通过逐层沉积生产复杂自由形态零件的新兴数字制造技术。高沉积速率机器人增材制造（HDRRAM），如冷喷涂增材制造（CSAM），通过单位时间内输送大量材料显著提高制造速度。然而，当前开环系统中的过程不稳定性导致保持形状精度仍面临重大挑战。在制造过程中实时检测这些偏差对于防止误差传播、保证零件质量及减少后续加工至关重要。本研究提出一种实时监控系统，用于获取并重建正在生长的零件，并直接与近净形参考模型进行比较，以在制造过程中检测形状偏差。早期识别形状不一致后，通过分割和追踪每个偏差区域，为及时干预和补偿提供可能，从而实现稳定的零件质量。
原文摘要 · Abstract (English)
Additive manufacturing (AM) is an emerging digital manufacturing technology to produce complex and freeform objects through a layer-wise deposition. High deposition rate robotic AM (HDRRAM) processes, such as cold spray additive manufacturing (CSAM), offer significantly increased 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 minimize post-processing requirements. This study presents a real-time monitoring system to acquire and reconstruct the growing part and directly compares it with a near-net reference model to detect the shape deviation during the manufacturing process. The early identification of shape inconsistencies, followed by segmenting and tracking each deviation region, paves the way for timely intervention and compensation to achieve consistent part quality.
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