Regression-Based Inductive Reconstruction of Shell Auxetic Structures
dc.catalogador | yvc | |
dc.contributor.author | Vivanco LarraĆn, Tomas | |
dc.contributor.author | Ojeda Valenzuela, Juan Eduardo | |
dc.contributor.author | Yuan, Philip | |
dc.contributor.other | Pontificia Universidad CatĆ³lica de Chile. Escuela de DiseƱo | |
dc.contributor.other | Tongji University | |
dc.contributor.other | Technical University Darmstadt | |
dc.date.accessioned | 2024-06-13T15:50:27Z | |
dc.date.available | 2024-06-13T15:50:27Z | |
dc.date.issued | 2023 | |
dc.description.abstract | This 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.objetodigital | 2024-06-13 | |
dc.fuente.origen | SIPA | |
dc.identifier.doi | 10.1007/978-981-19-8637-6_42 | |
dc.identifier.uri | https://link.springer.com/chapter/10.1007/978-981-19-8637-6_42 | |
dc.identifier.uri | https://repositorio.uc.cl/handle/11534/86754 | |
dc.information.autoruc | Escuela de DiseƱo; Vivanco LarraĆn, Tomas; 0000-0003-2746-3593; 1012501 | |
dc.information.autoruc | Escuela de Arquitectura; Ojeda Valenzuela, Juan Eduardo; S/I; 127430 | |
dc.issue.numero | 1 | |
dc.language.iso | und | |
dc.nota.acceso | contenido completo | |
dc.pagina.final | 498 | |
dc.pagina.inicio | 488 | |
dc.relation.ispartof | International Conference on Computational Design and Robotic Fabrication (CDRF) : 4th : 2022 : Singapore | |
dc.rights | acceso abierto | |
dc.rights.license | CC BY Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.subject | Artificial intelligence | |
dc.subject | Material computation | |
dc.subject | Auxetic structures | |
dc.subject | Computational design | |
dc.subject.ddc | 711 | |
dc.subject.dewey | Arquitectura | es_ES |
dc.title | Regression-Based Inductive Reconstruction of Shell Auxetic Structures | |
dc.type | comunicaciĆ³n de congreso | |
dc.volumen | 4 | |
sipa.codpersvinculados | 1012501 | |
sipa.codpersvinculados | 127430 |
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