Real-time detection of powder bed defects in laser powder bed fusion using deep learning on 3D point clouds

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...

Полное описание

Библиографические подробности
Главные авторы:	Junlai Zhao, Zihan Yang, Qingpeng Chen, Chen Zhang, Jianhui Zhao, Guoqing Zhang, Fang Dong, Sheng Liu
Формат:	Статья
Язык:	English
Опубликовано:	Taylor & Francis Group 2025-12-01
Серии:	Virtual and Physical Prototyping
Предметы:	Laser powder bed fusion (LPBF) powder bed defect detection 3D point cloud deep learning real-time monitoring
Online-ссылка:	https://www.tandfonline.com/doi/10.1080/17452759.2024.2449171

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