Vivanco, Tomás ; Ojeda, Juan Eduardo ; Yuan, Philip
Hrsg.: Yuan, Philip F. ; Chai, Hua ; Yan, Chao ; Li, Keke ; Sun, Tongyue (2023)
Regression-Based Inductive Reconstruction of Shell Auxetic Structures.
In: Hybrid Intelligence. CDRF 2022. Computational Design and Robotic Fabrication.
doi: 10.1007/978-981-19-8637-6_42
Buchkapitel, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Buchkapitel |
|---|---|
| Erschienen: | 2023 |
| Herausgeber: | Yuan, Philip F. ; Chai, Hua ; Yan, Chao ; Li, Keke ; Sun, Tongyue |
| Autor(en): | Vivanco, Tomás ; Ojeda, Juan Eduardo ; Yuan, Philip |
| Art des Eintrags: | Bibliographie |
| Titel: | Regression-Based Inductive Reconstruction of Shell Auxetic Structures |
| Sprache: | Englisch |
| Publikationsjahr: | 2023 |
| Ort: | Singapore |
| Verlag: | Springer Nature Singapore |
| Buchtitel: | Hybrid Intelligence. CDRF 2022. Computational Design and Robotic Fabrication. |
| DOI: | 10.1007/978-981-19-8637-6_42 |
| URL / URN: | https://link.springer.com/chapter/10.1007/978-981-19-8637-6_... |
| Kurzbeschreibung (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. |
| Fachbereich(e)/-gebiet(e): | 13 Fachbereich Bau- und Umweltingenieurwissenschaften 13 Fachbereich Bau- und Umweltingenieurwissenschaften > Institut für Statik und Konstruktion 13 Fachbereich Bau- und Umweltingenieurwissenschaften > Institut für Statik und Konstruktion > Fachgebiet Fassadentechnik |
| Hinterlegungsdatum: | 03 Mai 2023 08:54 |
| Letzte Änderung: | 03 Mai 2023 08:54 |
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