---
_id: '12976'
abstract:
- lang: eng
text: "3D printing based on continuous deposition of materials, such as filament-based
3D printing, has seen widespread adoption thanks to its versatility in working
with a wide range of materials. An important shortcoming of this type of technology
is its limited multi-material capabilities. While there are simple hardware designs
that enable multi-material printing in principle, the required software is heavily
underdeveloped. A typical hardware design fuses together individual materials
fed into a single chamber from multiple inlets before they are deposited. This
design, however, introduces a time delay between the intended material mixture
and its actual deposition. In this work, inspired by diverse path planning research
in robotics, we show that this mechanical challenge can be addressed via improved
printer control. We propose to formulate the search for optimal multi-material
printing policies in a reinforcement\r\nlearning setup. We put forward a simple
numerical deposition model that takes into account the non-linear material mixing
and delayed material deposition. To validate our system we focus on color fabrication,
a problem known for its strict requirements for varying material mixtures at a
high spatial frequency. We demonstrate that our learned control policy outperforms
state-of-the-art hand-crafted algorithms."
acknowledgement: This work is graciously supported by FWF Lise Meitner (Grant M 3319).
Kang Liao sincerely thank Emiliano Luci, Chunyu Lin, and Yao Zhao for their huge
support.
article_processing_charge: No
author:
- first_name: Kang
full_name: Liao, Kang
last_name: Liao
- first_name: Thibault
full_name: Tricard, Thibault
last_name: Tricard
- first_name: Michael
full_name: Piovarci, Michael
id: 62E473F4-5C99-11EA-A40E-AF823DDC885E
last_name: Piovarci
orcid: 0000-0002-5062-4474
- first_name: Hans-Peter
full_name: Seidel, Hans-Peter
last_name: Seidel
- first_name: Vahid
full_name: Babaei, Vahid
last_name: Babaei
citation:
ama: 'Liao K, Tricard T, Piovarci M, Seidel H-P, Babaei V. Learning deposition policies
for fused multi-material 3D printing. In: 2023 IEEE International Conference
on Robotics and Automation. Vol 2023. IEEE; 2023:12345-12352. doi:10.1109/ICRA48891.2023.10160465'
apa: 'Liao, K., Tricard, T., Piovarci, M., Seidel, H.-P., & Babaei, V. (2023).
Learning deposition policies for fused multi-material 3D printing. In 2023
IEEE International Conference on Robotics and Automation (Vol. 2023, pp. 12345–12352).
London, United Kingdom: IEEE. https://doi.org/10.1109/ICRA48891.2023.10160465'
chicago: Liao, Kang, Thibault Tricard, Michael Piovarci, Hans-Peter Seidel, and
Vahid Babaei. “Learning Deposition Policies for Fused Multi-Material 3D Printing.”
In 2023 IEEE International Conference on Robotics and Automation, 2023:12345–52.
IEEE, 2023. https://doi.org/10.1109/ICRA48891.2023.10160465.
ieee: K. Liao, T. Tricard, M. Piovarci, H.-P. Seidel, and V. Babaei, “Learning deposition
policies for fused multi-material 3D printing,” in 2023 IEEE International
Conference on Robotics and Automation, London, United Kingdom, 2023, vol.
2023, pp. 12345–12352.
ista: 'Liao K, Tricard T, Piovarci M, Seidel H-P, Babaei V. 2023. Learning deposition
policies for fused multi-material 3D printing. 2023 IEEE International Conference
on Robotics and Automation. ICRA: International Conference on Robotics and Automation
vol. 2023, 12345–12352.'
mla: Liao, Kang, et al. “Learning Deposition Policies for Fused Multi-Material 3D
Printing.” 2023 IEEE International Conference on Robotics and Automation,
vol. 2023, IEEE, 2023, pp. 12345–52, doi:10.1109/ICRA48891.2023.10160465.
short: K. Liao, T. Tricard, M. Piovarci, H.-P. Seidel, V. Babaei, in:, 2023 IEEE
International Conference on Robotics and Automation, IEEE, 2023, pp. 12345–12352.
conference:
end_date: 2023-06-02
location: London, United Kingdom
name: 'ICRA: International Conference on Robotics and Automation'
start_date: 2023-05-29
date_created: 2023-05-16T09:14:09Z
date_published: 2023-07-04T00:00:00Z
date_updated: 2023-12-13T11:20:00Z
day: '04'
ddc:
- '004'
department:
- _id: BeBi
doi: 10.1109/ICRA48891.2023.10160465
external_id:
isi:
- '001048371104068'
file:
- access_level: open_access
checksum: daeaa67124777d88487f933ea3f77164
content_type: application/pdf
creator: mpiovarc
date_created: 2023-05-16T09:12:05Z
date_updated: 2023-05-16T09:12:05Z
file_id: '12977'
file_name: Liao2023.pdf
file_size: 5367986
relation: main_file
success: 1
file_date_updated: 2023-05-16T09:12:05Z
has_accepted_license: '1'
intvolume: ' 2023'
isi: 1
keyword:
- reinforcement learning
- deposition
- control
- color
- multi-filament
language:
- iso: eng
month: '07'
oa: 1
oa_version: Submitted Version
page: 12345-12352
project:
- _id: eb901961-77a9-11ec-83b8-f5c883a62027
grant_number: M03319
name: Perception-Aware Appearance Fabrication
publication: 2023 IEEE International Conference on Robotics and Automation
publication_identifier:
eisbn:
- '9798350323658'
issn:
- 1050-4729
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Learning deposition policies for fused multi-material 3D printing
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2023
year: '2023'
...