{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,24]],"date-time":"2025-08-24T00:02:53Z","timestamp":1755993773845,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":22,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T00:00:00Z","timestamp":1721779200000},"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":[],"published-print":{"date-parts":[[2024,7,24]]},"DOI":"10.1145\/3688574.3688590","type":"proceedings-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T12:21:55Z","timestamp":1726230115000},"page":"111-117","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Topology Optimization of Industrial Equipment: A Deep Learning Approach and Software Implement"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8758-4226","authenticated-orcid":false,"given":"Zhiru","family":"Li","sequence":"first","affiliation":[{"name":"China Academy of Industrial Internet, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9420-1458","authenticated-orcid":false,"given":"Xiaohui","family":"Liu","sequence":"additional","affiliation":[{"name":"China Academy of Industrial Internet, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7279-731X","authenticated-orcid":false,"given":"Xiangman","family":"Song","sequence":"additional","affiliation":[{"name":"Northeastern University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5986-5716","authenticated-orcid":false,"given":"Weixi","family":"Gu","sequence":"additional","affiliation":[{"name":"China Academy of Industrial Internet, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4732-3915","authenticated-orcid":false,"given":"Guowei","family":"Zhu","sequence":"additional","affiliation":[{"name":"China Academy of Industrial Internet, China"}]}],"member":"320","published-online":{"date-parts":[[2024,9,13]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"3D topology optimization using convolutional neural networks.arXiv preprint arXiv:1808.07440","author":"Banga Saurabh","year":"2018","unstructured":"Saurabh Banga, Harsh Gehani, Sanket Bhilare, Sagar Patel, and Levent Kara. 2018. 3D topology optimization using convolutional neural networks.arXiv preprint arXiv:1808.07440 (2018)."},{"key":"e_1_3_2_1_2_1","volume-title":"Self-directed online machine learning for topology optimization. Nature communications 13, 1","author":"Deng Changyu","year":"2022","unstructured":"Changyu Deng, Yizhou Wang, Can Qin, Yun Fu, and Wei Lu. 2022. Self-directed online machine learning for topology optimization. Nature communications 13, 1 (2022), 388."},{"key":"e_1_3_2_1_3_1","volume-title":"Topology optimization based on deep representation learning (DRL) for compliance and stress-constrained design.Computational Mechanics 66, 2","author":"Deng Hao","year":"2020","unstructured":"Hao Deng and Albert C To. 2020. Topology optimization based on deep representation learning (DRL) for compliance and stress-constrained design.Computational Mechanics 66, 2 (2020), 449\u2013469."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00158-012-0781-9"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/nme.5432"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10409-010-0395-7"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.euromechsol.2021.104327"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.2351\/7.0001307"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-024-13054-4"},{"key":"e_1_3_2_1_10_1","volume-title":"Reducing the dimensionality of data with neural networks. science 313, 5786","author":"Hinton Geoffrey E","year":"2006","unstructured":"Geoffrey E Hinton and Ruslan R Salakhutdinov. 2006. Reducing the dimensionality of data with neural networks. science 313, 5786 (2006), 504\u2013507."},{"key":"e_1_3_2_1_11_1","volume-title":"Current and future trends of artificial intelligence in the field of structural topology optimization. Chinese Journal of Computational Mechanics","author":"Jun Yan","year":"2021","unstructured":"Yan Jun, Xu Qi, Zhang Qi, Fan Zhirui, Du Hongze, Geng Dongling, Yan Kun, and Niu Bin. 2021. Current and future trends of artificial intelligence in the field of structural topology optimization. Chinese Journal of Computational Mechanics (2021)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1115\/1.4037000"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0207(19980615)42:3<535::AID-NME372>3.0.CO;2-J"},{"key":"e_1_3_2_1_14_1","volume-title":"A critical review of established methods of structural topology optimization. Structural and multidisciplinary optimization 37","author":"Rozvany George IN","year":"2009","unstructured":"George IN Rozvany. 2009. A critical review of established methods of structural topology optimization. Structural and multidisciplinary optimization 37 (2009), 217\u2013237."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2017.03.006"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMAG.2019.2901906"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00158-013-0978-6"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1080\/0305215X.2021.1902998"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2018.09.007"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00158-016-1444-z"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12206-015-0729-2"},{"key":"e_1_3_2_1_22_1","volume-title":"A new three-dimensional topology optimization method based on moving morphable components (MMCs). Computational Me\u00b7chanics 59","author":"Zhang Weisheng","year":"2017","unstructured":"Weisheng Zhang, Dong Li, Jie Yuan, Junfu Song, and Xu Guo. 2017. A new three-dimensional topology optimization method based on moving morphable components (MMCs). Computational Me\u00b7chanics 59 (2017), 647\u2013665."}],"event":{"name":"BDE 2024: 2024 6th International Conference on Big Data Engineering","acronym":"BDE 2024","location":"Xining China"},"container-title":["Proceedings of the 2024 6th International Conference on Big Data Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3688574.3688590","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3688574.3688590","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T02:14:23Z","timestamp":1755915263000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3688574.3688590"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,24]]},"references-count":22,"alternative-id":["10.1145\/3688574.3688590","10.1145\/3688574"],"URL":"https:\/\/doi.org\/10.1145\/3688574.3688590","relation":{},"subject":[],"published":{"date-parts":[[2024,7,24]]},"assertion":[{"value":"2024-09-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}