MOSAIC实时分割激光熔融中复杂成像下的匙孔，提升工艺监控效率。
MOSAIC: Mobile Object Segmentation under Adverse Imaging Conditions for Rapid L-PBF Keyhole Behavior Characterization
- 基于移动目标分割，无需人工标注即可快速分析匙孔动态。
- 在12组样本上平均F1达0.894，精度0.953，优于SAM与YOLO。
- 单帧处理仅需19.9毫秒，适合现场实时监测，适配边缘计算。
在激光粉末床熔融（L-PBF）过程中，气体与流体相互作用的快速演变使过程监控与控制变得复杂，不稳定的匙孔会导致气孔和飞溅。利用高速原位X射线成像可更好地理解这些相互作用对工艺的影响。MOSAIC是一种针对恶劣成像条件下实验设计的移动目标分割算法，可在无需耗时人工标注或模型训练的情况下，实现在活跃光束线实验中的匙孔动力学快速分析。对12个独立样品的验证表明，其平均F1分数为0.894，精度为0.953，优于或等同于测试的SAM与YOLO机器学习方法。MOSAIC高效运行，对约150×250像素的移动窗口图像，在CPU上每帧处理仅需19.9毫秒，而YOLO和SAM模型在相同硬件上的推理时间分别为54毫秒和5284毫秒。
原文摘要 · Abstract (English)
In laser powder bed fusion (L-PBF) processes, the rapid evolution of gas and fluid interactions complicates our ability to properly monitor or control the process, with unstable keyholes leading to porosity and spatter formation. High-speed operando x-ray imaging of the keyhole has been used to better understand the impact of these interactions on the monitoring and control of the L-PBF process. MOSAIC, a Mobile Object Segmentation algorithm for experiments under Adverse Imaging Conditions, is designed to perform rapid analysis of keyhole dynamics during active beamline experimentation without needing time consuming manual labeling or model training. Validation studies performed on 12 unique samples proved the robustness of MOSAIC with an average F1 score of 0.894 and a precision of 0.953 when compared to manually segmented images, performing equally or better than the SAM and YOLO machine learning methods tested. MOSAIC is efficient, processing frames cropped to a moving window approximately 150x250 pixels at 19.9 milliseconds per image on CPU, compared to 54 and 5284 milliseconds per image for inference on CPU for YOLO and SAM models.
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