{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T05:39:16Z","timestamp":1676093956116},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643683782","type":"print"},{"value":"9781643683799","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T00:00:00Z","timestamp":1675641600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,2,6]]},"abstract":"<jats:p>A neural network used for structural topology optimization is designed and built in this paper. In the proposed method, the density-based SIMP method is first applied to obtain the structural data of three different mechanisms with specified design conditions. Image processing and data screening methods are used to obtain raw material for the learning and training process. Then, a neural network is built according to the funnel-shaped neural network structure, whose tasks is to optimize the network for the purpose of outputting the structural model, a lightweight algorithm is used to improve generalization and effectiveness of the network. The effectiveness of the proposed method is verified through the classic minimum compliance optimization problems.<\/jats:p>","DOI":"10.3233\/faia230065","type":"book-chapter","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T06:57:00Z","timestamp":1676012220000},"source":"Crossref","is-referenced-by-count":0,"title":["Density Topology Optimization Method Based on Neural Network"],"prefix":"10.3233","author":[{"given":"Longfei","family":"Qie","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Xing","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Digitalization and Management Innovation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230065","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T06:57:02Z","timestamp":1676012222000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230065"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,6]]},"ISBN":["9781643683782","9781643683799"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230065","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,6]]}}}