Regression-Based Inductive Reconstruction of Shell Auxetic Structures

dc.catalogadoryvc
dc.contributor.authorVivanco LarraĆ­n, Tomas
dc.contributor.authorOjeda Valenzuela, Juan Eduardo
dc.contributor.authorYuan, Philip
dc.contributor.otherPontificia Universidad CatĆ³lica de Chile. Escuela de DiseƱo
dc.contributor.otherTongji University
dc.contributor.otherTechnical University Darmstadt
dc.date.accessioned2024-06-13T15:50:27Z
dc.date.available2024-06-13T15:50:27Z
dc.date.issued2023
dc.description.abstractThis article presents the design process for generating a shell-like structure from an activated bent auxetic surface through an inductive process based on applying deep learning algorithms to predict a numeric value of geometrical features. The process developed under the Material Intelligence Workflow applied to the development of (1) a computational simulation of the mechanical and physical behaviour of an activated auxetic surface, (2) the generation of a geometrical dataset composed of six geometric features with 3,000 values each, (3) the construction and training of a regression Deep Neuronal Network (DNN) model, (4) the prediction of the geometric feature of the auxetic surface's pattern distance, and (5) the reconstruction of a new shell based on the predicted value. This process consistently reduces the computational power and simulation time to produce digital prototypes by integrating AI-based algorithms into material computation design processes.
dc.fechaingreso.objetodigital2024-06-13
dc.fuente.origenSIPA
dc.identifier.doi10.1007/978-981-19-8637-6_42
dc.identifier.urihttps://link.springer.com/chapter/10.1007/978-981-19-8637-6_42
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/86754
dc.information.autorucEscuela de DiseƱo; Vivanco Larraƭn, Tomas; 0000-0003-2746-3593; 1012501
dc.information.autorucEscuela de Arquitectura; Ojeda Valenzuela, Juan Eduardo; S/I; 127430
dc.issue.numero1
dc.language.isound
dc.nota.accesocontenido completo
dc.pagina.final498
dc.pagina.inicio488
dc.relation.ispartofInternational Conference on Computational Design and Robotic Fabrication (CDRF) : 4th : 2022 : Singapore
dc.rightsacceso abierto
dc.rights.licenseCC BY Attribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectArtificial intelligence
dc.subjectMaterial computation
dc.subjectAuxetic structures
dc.subjectComputational design
dc.subject.ddc711
dc.subject.deweyArquitecturaes_ES
dc.titleRegression-Based Inductive Reconstruction of Shell Auxetic Structures
dc.typecomunicaciĆ³n de congreso
dc.volumen4
sipa.codpersvinculados1012501
sipa.codpersvinculados127430
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