---
_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: <i>2023 IEEE International Conference
    on Robotics and Automation</i>. Vol 2023. IEEE; 2023:12345-12352. doi:<a href="https://doi.org/10.1109/ICRA48891.2023.10160465">10.1109/ICRA48891.2023.10160465</a>'
  apa: 'Liao, K., Tricard, T., Piovarci, M., Seidel, H.-P., &#38; Babaei, V. (2023).
    Learning deposition policies for fused multi-material 3D printing. In <i>2023
    IEEE International Conference on Robotics and Automation</i> (Vol. 2023, pp. 12345–12352).
    London, United Kingdom: IEEE. <a href="https://doi.org/10.1109/ICRA48891.2023.10160465">https://doi.org/10.1109/ICRA48891.2023.10160465</a>'
  chicago: Liao, Kang, Thibault Tricard, Michael Piovarci, Hans-Peter Seidel, and
    Vahid Babaei. “Learning Deposition Policies for Fused Multi-Material 3D Printing.”
    In <i>2023 IEEE International Conference on Robotics and Automation</i>, 2023:12345–52.
    IEEE, 2023. <a href="https://doi.org/10.1109/ICRA48891.2023.10160465">https://doi.org/10.1109/ICRA48891.2023.10160465</a>.
  ieee: K. Liao, T. Tricard, M. Piovarci, H.-P. Seidel, and V. Babaei, “Learning deposition
    policies for fused multi-material 3D printing,” in <i>2023 IEEE International
    Conference on Robotics and Automation</i>, 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.” <i>2023 IEEE International Conference on Robotics and Automation</i>,
    vol. 2023, IEEE, 2023, pp. 12345–52, doi:<a href="https://doi.org/10.1109/ICRA48891.2023.10160465">10.1109/ICRA48891.2023.10160465</a>.
  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'
...