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...
| 主要な著者: | , , , , , , , |
|---|---|
| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Taylor & Francis Group
2025-12-01
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| シリーズ: | Virtual and Physical Prototyping |
| 主題: | |
| オンライン･アクセス: | https://www.tandfonline.com/doi/10.1080/17452759.2024.2449171 |