Design of nonlinear gradient sheet-based TPMS-lattice using artificial neural networks

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dc.contributor.authorLi, Zhou
dc.contributor.authorLi, Junhao
dc.contributor.authorTian, Jiahao
dc.contributor.authorXia, Shiqi
dc.contributor.authorLi, Kai
dc.contributor.authorSu, Guanqiao
dc.contributor.authorLu, Yao
dc.contributor.authorRen, Mengyuan
dc.contributor.authorJiang, Zhengyi
dc.date.accessioned2024-10-21T14:19:38Z
dc.date.available2024-10-21T14:19:38Z
dc.date.freetoread2024-10-21
dc.date.issued2024-11-01
dc.date.pubOnline2024-09-14
dc.description.abstractGradient triply periodic minimal surface (TPMS) structures are renowned for lightweight design and enhanced performance, but their complex and nonlinear configurations pose challenges in achieving targeted design goals. A new design methodology for the nonlinear gradient structure was proposed in this study, with the aim of achieving efficient and accurate modeling of complex and gradient sheet-based TPMS structures under specific performance objectives. This method utilized automated finite element (FE) simulations to obtain structure topology element densities under various boundary conditions. An artificial neural network (ANN) was then employed to efficiently predict the correspondence between these boundary conditions and topology element densities. A mapping was established between topology element densities and TPMS structural parameters, and the gradient structure was accurately constructed by using the voxel modeling technique. Taking a typical cantilever beam TPMS structure as an example of nonlinear gradient design, the results indicate that the error between the ANN-predicted and FE-simulated structure topology element densities is only 2.73 %, with prediction time being only 0.15 % of the simulation time. The thin regions of the gradient structure align with those geometrically removed in regular topology optimization scheme, achieving up to 65.45 % weight reduction, a 28.72 % improvement over the regular scheme, along with uniform structural stress transition and maximum stress reduction. TC4 alloy nonlinear gradient TPMS structures, printed by metal selective laser melting (SLM) technique, confirm the practical application value of this design method.
dc.description.journalNameJournal of Materials Research and Technology
dc.description.sponsorshipNational Natural Science Foundation of China
dc.description.sponsorshipThe authors wish to gratefully acknowledge the financial support from the National Natural Science Foundation of China (Grant No. 52105418), the Natural Science Foundation of Hunan Province (Grant No. 2023JJ20069, 2023JJ40752 and 2022JJ40600), and the key scientific research project of Hunan Provincial Department of Education (Grant No. 23A0001).
dc.format.extentpp. 223-234
dc.identifier.citationLi Z, Li J, Tian J, et al., (2024) Design of nonlinear gradient sheet-based TPMS-lattice using artificial neural networks. Journal of Materials Research and Technology, Volume 33, November-December 2024, pp. 223-234
dc.identifier.eissn2214-0697
dc.identifier.elementsID553961
dc.identifier.issn2238-7854
dc.identifier.urihttps://doi.org/10.1016/j.jmrt.2024.09.051
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23043
dc.identifier.volumeNo33
dc.languageEnglish
dc.language.isoen
dc.publisherElsevier
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S223878542402057X?via%3Dihub
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectTriply periodic minimal surface (TPMS)
dc.subjectVoxel modeling
dc.subjectTopology element density
dc.subjectNeural networks
dc.subjectAutomated finite element simulation
dc.subjectAdditive manufacturing
dc.subject40 Engineering
dc.subject4001 Aerospace Engineering
dc.subjectMachine Learning and Artificial Intelligence
dc.subject40 Engineering
dc.titleDesign of nonlinear gradient sheet-based TPMS-lattice using artificial neural networks
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2024-09-09

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