{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T16:22:48Z","timestamp":1777652568141,"version":"3.51.4"},"reference-count":56,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T00:00:00Z","timestamp":1731974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2024,12,19]]},"abstract":"<jats:p>\n            This paper presents a learning based planner for computing optimized 3D printing toolpaths on prescribed graphs, the challenges of which include the varying graph structures on different models and the large scale of nodes &amp; edges on a graph. We adopt an on-the-fly strategy to tackle these challenges, formulating the planner as a\n            <jats:italic toggle=\"yes\">Deep Q-Network<\/jats:italic>\n            (DQN) based optimizer to decide the next 'best' node to visit. We construct the state spaces by the\n            <jats:italic toggle=\"yes\">Local Search Graph<\/jats:italic>\n            (LSG) centered at different nodes on a graph, which is encoded by a carefully designed algorithm so that LSGs in similar configurations can be identified to re-use the earlier learned DQN priors for accelerating the computation of toolpath planning. Our method can cover different 3D printing applications by defining their corresponding reward functions. Toolpath planning problems in wire-frame printing, continuous fiber printing, and metallic printing are selected to demonstrate its generality. The performance of our planner has been verified by testing the resultant toolpaths in physical experiments. By using our planner, wire-frame models with up to 4.2k struts can be successfully printed, up to 93.3% of sharp turns on continuous fiber toolpaths can be avoided, and the thermal distortion in metallic printing can be reduced by 24.9%.\n          <\/jats:p>","DOI":"10.1145\/3687933","type":"journal-article","created":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T10:46:04Z","timestamp":1732013164000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Learning Based Toolpath Planner on Diverse Graphs for 3D Printing"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5900-2164","authenticated-orcid":false,"given":"Yuming","family":"Huang","sequence":"first","affiliation":[{"name":"University of Manchester, Manchester, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5398-6845","authenticated-orcid":false,"given":"Yuhu","family":"Guo","sequence":"additional","affiliation":[{"name":"University of Manchester, Manchester, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0767-6209","authenticated-orcid":false,"given":"Renbo","family":"Su","sequence":"additional","affiliation":[{"name":"University of Manchester, Manchester, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1899-1952","authenticated-orcid":false,"given":"Xingjian","family":"Han","sequence":"additional","affiliation":[{"name":"Boston University, Boston, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5741-8115","authenticated-orcid":false,"given":"Junhao","family":"Ding","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0372-0049","authenticated-orcid":false,"given":"Tianyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Manchester, Manchester, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1016-4191","authenticated-orcid":false,"given":"Tao","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Manchester, Manchester, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6289-0094","authenticated-orcid":false,"given":"Weiming","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Manchester, Manchester, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8741-3227","authenticated-orcid":false,"given":"Guoxin","family":"Fang","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9811-9883","authenticated-orcid":false,"given":"Xu","family":"Song","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7997-1675","authenticated-orcid":false,"given":"Emily","family":"Whiting","sequence":"additional","affiliation":[{"name":"Boston University, Boston, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4406-8480","authenticated-orcid":false,"given":"Charlie","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Manchester, Manchester, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,11,19]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2022.102822"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cad.2021.103122"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10514-020-09947-4"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2021.102470"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2019.2931199"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3414685.3417834"},{"key":"e_1_2_2_7_1","volume-title":"Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.","author":"Fey Matthias","unstructured":"Matthias Fey and Jan E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds."},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gmod.2019.101034"},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0218195921500096"},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/261342.571216"},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2023.103501"},{"key":"e_1_2_2_12_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2980179.2982401","article-title":"Framefab: Robotic fabrication of frame shapes","volume":"35","author":"Huang Yijiang","year":"2016","unstructured":"Yijiang Huang, Juyong Zhang, Xin Hu, Guoxian Song, Zhongyuan Liu, Lei Yu, and Ligang Liu. 2016. Framefab: Robotic fabrication of frame shapes. ACM Transactions on Graphics (TOG) 35, 6 (2016), 1--11.","journal-title":"ACM Transactions on Graphics (TOG)"},{"key":"e_1_2_2_13_1","volume-title":"Offline reinforcement learning as one big sequence modeling problem. Advances in neural information processing systems 34","author":"Janner Michael","year":"2021","unstructured":"Michael Janner, Qiyang Li, and Sergey Levine. 2021. Offline reinforcement learning as one big sequence modeling problem. Advances in neural information processing systems 34 (2021), 1273--1286."},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12444"},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10601-022-09327-y"},{"key":"e_1_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2016.09.046"},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00466-021-02079-1"},{"key":"e_1_2_2_18_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=SJU4ayYgl","author":"Thomas","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=SJU4ayYgl"},{"key":"e_1_2_2_19_1","volume-title":"International Conference on Learning Representations.","author":"Kool Wouter","year":"2018","unstructured":"Wouter Kool, Herke van Hoof, and Max Welling. 2018. Attention, Learn to Solve Routing Problems!. In International Conference on Learning Representations."},{"key":"e_1_2_2_20_1","volume-title":"Randomized kinodynamic planning. The international journal of robotics research 20, 5","author":"LaValle Steven M","year":"2001","unstructured":"Steven M LaValle and James J Kuffner Jr. 2001. Randomized kinodynamic planning. The international journal of robotics research 20, 5 (2001), 378--400."},{"key":"e_1_2_2_21_1","volume-title":"Introduction to algorithms (3 ed.)","author":"Leiserson Charles Eric","unstructured":"Charles Eric Leiserson, Ronald L Rivest, Thomas H Cormen, and Clifford Stein. 1994. Introduction to algorithms (3 ed.). MIT press."},{"key":"e_1_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/566654.566590"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48891.2023.10160465"},{"key":"e_1_2_2_24_1","volume-title":"Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971","author":"Lillicrap Timothy P","year":"2015","unstructured":"Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2015. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971 (2015)."},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2918703"},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2018.09.019"},{"key":"e_1_2_2_27_1","volume-title":"Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602","author":"Mnih Volodymyr","year":"2013","unstructured":"Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. 2013. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013)."},{"key":"e_1_2_2_28_1","doi-asserted-by":"crossref","unstructured":"Volodymyr Mnih Koray Kavukcuoglu David Silver Andrei A Rusu Joel Veness Marc G Bellemare Alex Graves Martin Riedmiller Andreas K Fidjeland Georg Ostrovski et al. 2015. Human-level control through deep reinforcement learning. nature 518 7540 (2015) 529--533.","DOI":"10.1038\/nature14236"},{"key":"e_1_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2020.101265"},{"key":"e_1_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2004.838008"},{"key":"e_1_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmapro.2015.06.009"},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2869644"},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3528223.3530144"},{"key":"e_1_2_2_34_1","volume-title":"Charlie CL Wang, and Wei-Hsin Liao","author":"Qin Mian","year":"2023","unstructured":"Mian Qin, Junhao Ding, Shuo Qu, Xu Song, Charlie CL Wang, and Wei-Hsin Liao. 2023a. Deep Reinforcement Learning Based Toolpath Generation for Thermal Uniformity in Laser Powder Bed Fusion Process. Additive Manufacturing (2023), 103937."},{"key":"e_1_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2023.103432"},{"key":"e_1_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.msea.2013.04.099"},{"key":"e_1_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2022.102643"},{"key":"e_1_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.3390\/jcs4030098"},{"key":"e_1_2_2_39_1","volume-title":"Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al.","author":"Silver David","year":"2016","unstructured":"David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al. 2016. Mastering the game of Go with deep neural networks and tree search. Nature 529, 7587 (2016), 484--489."},{"key":"e_1_2_2_40_1","doi-asserted-by":"crossref","unstructured":"David Silver Julian Schrittwieser Karen Simonyan Ioannis Antonoglou Aja Huang Arthur Guez Thomas Hubert Lucas Baker Matthew Lai Adrian Bolton et al. 2017. Mastering the game of go without human knowledge. Nature 550 7676 (2017) 354--359.","DOI":"10.1038\/nature24270"},{"key":"e_1_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3623263.3623356"},{"key":"e_1_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2018.2878318"},{"key":"e_1_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.3026638"},{"key":"e_1_2_2_44_1","volume-title":"A globally conforming lattice structure for 2D stress tensor visualization. Computer graphics forum 39, 3","author":"Wang Junpeng","year":"2020","unstructured":"Junpeng Wang, Jun Wu, and R\u00fcdiger Westermann. 2020b. A globally conforming lattice structure for 2D stress tensor visualization. Computer graphics forum 39, 3 (2020), 417--427."},{"key":"e_1_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/2508363.2508413"},{"key":"e_1_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2019.100898"},{"key":"e_1_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2019.2924125"},{"key":"e_1_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925966"},{"key":"e_1_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2022.102816"},{"key":"e_1_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/1037957.1037958"},{"key":"e_1_2_2_51_1","volume-title":"A graph-based path planning method for additive manufacturing of continuous fiber-reinforced planar thin-walled cellular structures. Rapid Prototyping Journal ahead-of-print","author":"Zhang Guoquan","year":"2022","unstructured":"Guoquan Zhang, Yaohui Wang, Jian He, and Yi Xiong. 2022. A graph-based path planning method for additive manufacturing of continuous fiber-reinforced planar thin-walled cellular structures. Rapid Prototyping Journal ahead-of-print (2022)."},{"key":"e_1_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1108\/RPJ-01-2022-0027"},{"key":"e_1_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2020.101775"},{"key":"e_1_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3414685.3417831"},{"key":"e_1_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925958"},{"key":"e_1_2_2_56_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3197517.3201338","article-title":"DSCarver: decompose-and-spiral-carve for subtractive manufacturing","volume":"37","author":"Zhao Haisen","year":"2018","unstructured":"Haisen Zhao, Hao Zhang, Shiqing Xin, Yuanmin Deng, Changhe Tu, Wenping Wang, Daniel Cohen-Or, and Baoquan Chen. 2018. DSCarver: decompose-and-spiral-carve for subtractive manufacturing. ACM Transactions on Graphics (TOG) 37, 4 (2018), 1--14.","journal-title":"ACM Transactions on Graphics (TOG)"}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3687933","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3687933","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T20:56:59Z","timestamp":1759265819000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3687933"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,19]]},"references-count":56,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,12,19]]}},"alternative-id":["10.1145\/3687933"],"URL":"https:\/\/doi.org\/10.1145\/3687933","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,19]]},"assertion":[{"value":"2024-11-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}