--- _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' ...