Robotic Depowdering for Additive Manufacturing Via Pose Tracking

With the rapid development of powder-based additive manufacturing, depowdering, a process of removing unfused powder that covers 3D-printed parts, has become a major bottleneck to further improve its productiveness. Traditional manual depowdering is extremely time-consuming and costly, and some prio...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2022-10, Vol.7 (4), p.1-8
Main Authors: Liu, Zhenwei, Geng, Junyi, Dai, Xikai, Swierzewski, Tomasz, Shimada, Kenji
Format: Article
Language:English
Subjects:
Additive manufacturing
Automation
computer vision for manufacturing
Industrial robots
Manufacturing
Modules
Point cloud compression
Powders
Robots
Service robotics
Shape
Solid modeling
Surface treatment
Three dimensional printing
Tracking
Training
Visual perception
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Summary:With the rapid development of powder-based additive manufacturing, depowdering, a process of removing unfused powder that covers 3D-printed parts, has become a major bottleneck to further improve its productiveness. Traditional manual depowdering is extremely time-consuming and costly, and some prior automated systems either require pre-depowdering or lack adaptability to different 3D-printed parts. To solve these problems, we introduce a robotic system that automatically removes unfused powder from the surface of 3D-printed parts. The key component is a visual perception system, which consists of a pose-tracking module that tracks the 6D pose of powder-occluded parts in real-time, and a progress estimation module that estimates the depowdering completion percentage. The tracking module can be run efficiently on a laptop CPU at up to 60 FPS. Experiments show that our system can remove unfused powder from the surface of various 3D-printed parts without causing any damage. To the best of our knowledge, this is one of the first vision-based depowdering systems that adapt to parts with various shapes without the need for pre-depowdering.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2022.3195189