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...

وصف كامل

التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Junlai Zhao, Zihan Yang, Qingpeng Chen, Chen Zhang, Jianhui Zhao, Guoqing Zhang, Fang Dong, Sheng Liu
التنسيق: مقال
اللغة:English
منشور في: Taylor & Francis Group 2025-12-01
سلاسل:Virtual and Physical Prototyping
الموضوعات:
Laser powder bed fusion (LPBF)
powder bed defect detection
3D point cloud
deep learning
real-time monitoring
الوصول للمادة أونلاين:https://www.tandfonline.com/doi/10.1080/17452759.2024.2449171

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