Development of a Robotic System for Automated Decaking of 3D-Printed Parts
This addresses a specific problem for 3D-printing-based mass manufacturing, but it is incremental as it applies existing methods like Deep Learning and robotics to a new application.
The paper tackled the bottleneck of manual decaking in 3D-printing mass manufacturing by developing a robotic system that automates the process, demonstrating feasibility through experiments on Multi Jet Fusion printed parts.
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.