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Yeo Jung Yoon

dblp:246/7667 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Motion planning and robot control · 87% Robot manipulation · 13%
Computer graphics and multimedia
1 paper
Computational fabrication · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › trajectory planning
collision-free trajectory generation
0.412019
A Robotic Cell for Multi-Resolution Additive Manufacturing · ICRA 2019
Robotics › Motion planning and robot control
motion planning
0.412019
A Robotic Cell for Multi-Resolution Additive Manufacturing · ICRA 2019
Computational fabrication
additive manufacturing
0.412019
A Robotic Cell for Multi-Resolution Additive Manufacturing · ICRA 2019
Robotics › Robot manipulation › robot manipulator
dual-arm manipulator
0.112019
A Robotic Cell for Multi-Resolution Additive Manufacturing · ICRA 2019

Methods — techniques the papers use, named apart from their topics

part decomposition · 0.8non-planar layer generation · 0.8
YearPublicationVenuePosition
2019 A Robotic Cell for Multi-Resolution Additive Manufacturing
abstract
Extrusion-based additive manufacturing (AM), also known as fused deposition modeling (FDM) extrudes filaments through a heated nozzle and builds a part layer-by-layer. Using a smaller diameter nozzle can achieve better surface finish. However, there is a trade-off between surface finish and build times as using a small diameter nozzle leads to smaller layer thickness and long build times. Traditional FDM printers create a part with planar layers, and this restricts control over fiber orientations. This paper presents a robotic cell for multi-resolution AM. The cell consists of two 6 degrees of freedom (DOF) robot manipulators capable of printing non-planar and/or planar layers. We describe algorithms for decomposing parts into multi-resolution layers and generating collision-free trajectories for the robot manipulators. We validate our approach by printing five parts with multi-resolution.
Prahar M. Bhatt, Ariyan M. Kabir, Rishi K. Malhan, Brual C. Shah, Aniruddha V. Shembekar, Yeo Jung Yoon, Satyandra K. Gupta
ICRA6