Development of a Robotic System for Automated Decaking of 3D-Printed Parts

被引:0
作者
Huy Nguyen [1 ,2 ]
Adrian, Nicholas [1 ,2 ]
Yan, Joyce Lim Xin [1 ,2 ]
Salfity, Jonathan M. [1 ,3 ]
Allen, William [1 ,3 ]
Pham, Quang-Cuong [1 ,2 ]
机构
[1] Nanyang Technol Univ, HP NTU Digital Mfg Corp Lab, Singapore, Singapore
[2] Nanyang Technol Univ, Sch Mech & Aerosp Engn, Singapore, Singapore
[3] HP Inc, HP Labs, Palo Alto, CA USA
来源
2020 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) | 2020年
关键词
deep learning; manipulation; system design; 3D-printing; decaking;
D O I
10.1109/icra40945.2020.9197110
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the rapid rise of 3D-printing as a competitive mass manufacturing method, manual "decaking" - i.e. removing the residual powder that sticks to a 3D-printed part - has become a significant bottleneck. Here, we introduce, for the first time to our knowledge, a robotic system for automated decaking of 3D-printed parts. Combining Deep Learning for 3D perception, smart mechanical design, motion planning, and force control for industrial robots, we developed a system that can automatically decake parts in a fast and efficient way. Through a series of decaking experiments performed on parts printed by a Multi Jet Fusion printer, we demonstrated the feasibility of robotic decaking for 3D-printing-based mass manufacturing.
引用
收藏
页码:8202 / 8208
页数:7
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