2020/03/11 by Huy Nguyen, Nguyen, Huy, Nicholas Adrian +9
Engineering · #Additive Manufacturing Materials and Processes #Additive Manufacturing and 3D Printing Technologies #FOS: Computer and information sciences #Robot Manipulation and Learning #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2003.05115
openalex publication_date 2020/03/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With the rapid rise of 3D-printing as a competitive mass manufacturing\nmethod, manual "decaking" - i.e. removing the residual powder that sticks to a\n3D-printed part - has become a significant bottleneck. Here, we introduce, for\nthe first time to our knowledge, a robotic system for automated decaking of\n3D-printed parts. Combining Deep Learning for 3D perception, smart mechanical\ndesign, motion planning, and force control for industrial robots, we developed\na system that can automatically decake parts in a fast and efficient way.\nThrough a series of decaking experiments performed on parts printed by a Multi\nJet Fusion printer, we demonstrated the feasibility of robotic decaking for\n3D-printing-based mass manufacturing.\n