Real-time detection of powder bed defects in laser powder bed fusion using deep learning on 3D point clouds
Powder bed defects are critical factors affecting the print quality and stability in Laser Powder Bed Fusion (LPBF). However, traditional 2D image-based powder bed defect monitoring methods are limited by sensitivity to lighting conditions and insufficient data capture. This study proposes a real-ti...
| Main Authors: | , , , , , , , |
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| 格式: | Article |
| 語言: | English |
| 出版: |
Taylor & Francis Group
2025-12-01
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| 叢編: | Virtual and Physical Prototyping |
| 主題: | |
| 在線閱讀: | https://www.tandfonline.com/doi/10.1080/17452759.2024.2449171 |