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Robotic Depowdering for Additive Manufacturing Via Pose Tracking

Zhenwei Liu, Junyi Geng, Xikai Dai, Tomasz Swierzewski, Kenji Shimada

Year
2022
Citations
10

Abstract

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.

Keywords

LaptopBottleneckComputer science3d printedComputer visionProcess (computing)Artificial intelligenceComponent (thermodynamics)PoseTracking (education)

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