Inverse design of cellular structures with the geometry of triply periodic minimal surfaces using generative artificial intelligence algorithms

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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.authorLi, Maojun
dc.contributor.authorLu, Yao
dc.contributor.authorRen, Mengyuan
dc.contributor.authorJiang, Zhengyi
dc.date.accessioned2024-10-18T15:25:30Z
dc.date.available2024-10-18T15:25:30Z
dc.date.freetoread2024-10-18
dc.date.issued2024-12-15
dc.date.pubOnline2024-09-20
dc.description.abstractTriply periodic minimal surfaces (TPMS) exhibit excellent mechanical and energy absorption properties due to their structural advantages. However, existing porous TPMS structural design methods are constrained to a forward process from structural parameters to mechanical properties. This study proposed an inverse design method that combines bidirectional generative adversarial networks (BiGAN) and mechanical performance targets, resulting in a combined TPMS structure of Primitive and IWP types with superior buffering and energy absorption capabilities. The results show that under a single load value target condition of the designed structure, the minimum deviation index (R2) between the load value corresponding to the displacement point and the target load value is only 0.987, and the maximum mean absolute percentage error (MAPE) is only 5.92 %. When considering the elastic modulus target, the approach successfully conducts two sets of combined structural designs meeting the requirements of both high and low elastic moduli. When targeting the specified load-displacement curve conditions, specifically when combining high elastic modulus with ascending plasticity, the designed structures exhibit an error of only 2.2 % compared to the target property. Moreover, the quasi-static uniaxial compression experiments conducted on additively manufactured designed structures confirm that the experimental curves match the target curves in terms of deformation trends and load value ranges. The success of this inverse design approach for cellular TPMS structures has the potential to expedite new structural material development processes.
dc.description.journalNameEngineering Structures
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 and 2022JJ40600), and the Key Scientific Research Project of Hunan Provincial Department of Education (Grant No. 23A0001).
dc.identifier.citationLi Z, Li J, Tian J, et al., (2024) Inverse design of cellular structures with the geometry of triply periodic minimal surfaces using generative artificial intelligence algorithms. Engineering Structures, Volume 321, December 2024, Article number 118988
dc.identifier.eissn1873-7323
dc.identifier.elementsID553959
dc.identifier.issn0141-0296
dc.identifier.paperNo118988
dc.identifier.urihttps://doi.org/10.1016/j.engstruct.2024.118988
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23085
dc.identifier.volumeNo321
dc.languageEnglish
dc.language.isoen
dc.publisherElsevier
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S0141029624015505?via%3Dihub
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectTriply periodic minimal surface
dc.subjectGenerative artificial intelligence algorithms
dc.subjectAdditive manufacturing
dc.subjectInverse design
dc.subjectNumerical simulation
dc.subject4005 Civil Engineering
dc.subject40 Engineering
dc.subject4016 Materials Engineering
dc.subject7 Affordable and Clean Energy
dc.subjectCivil Engineering
dc.subject4005 Civil engineering
dc.subject4016 Materials engineering
dc.titleInverse design of cellular structures with the geometry of triply periodic minimal surfaces using generative artificial intelligence algorithms
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2024-09-15

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