Peng, Xiang-Long ; Xu, Bai-Xiang (2024)
Data-driven inverse design of composite triangular lattice structures.
In: International Journal of Mechanical Sciences, 265
doi: 10.1016/j.ijmecsci.2023.108900
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
In this work, we introduce a class of novel bi-material composite triangular lattice structures. The inverse design of these structures is achieved by using a data-driven method. They exhibit a broad range of tunable effective elastic properties, i.e., the effective Young’s and shear moduli span a few orders of magnitude, and the effective Poisson’s ratio can be both negative and positive. We exploit the computational homogenization method to calculate the effective elastic constants of these structures with varying structural features to generate a representative dataset. Subsequently, we harness the dataset to train artificial neural network models for both forward prediction and inverse design. The forward model predicts the effective properties of a given structure, while the inverse model generates a structure design for the specified target properties. We validate the performance of these models by several examples such as optimizing the isotropic auxetic properties. The data-driven surrogate models greatly facilitate the practical application of these novel lattice structures for various structural and/or functional purposes.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Peng, Xiang-Long ; Xu, Bai-Xiang |
| Art des Eintrags: | Bibliographie |
| Titel: | Data-driven inverse design of composite triangular lattice structures |
| Sprache: | Englisch |
| Publikationsjahr: | März 2024 |
| Verlag: | Elsevier |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | International Journal of Mechanical Sciences |
| Jahrgang/Volume einer Zeitschrift: | 265 |
| DOI: | 10.1016/j.ijmecsci.2023.108900 |
| Kurzbeschreibung (Abstract): | In this work, we introduce a class of novel bi-material composite triangular lattice structures. The inverse design of these structures is achieved by using a data-driven method. They exhibit a broad range of tunable effective elastic properties, i.e., the effective Young’s and shear moduli span a few orders of magnitude, and the effective Poisson’s ratio can be both negative and positive. We exploit the computational homogenization method to calculate the effective elastic constants of these structures with varying structural features to generate a representative dataset. Subsequently, we harness the dataset to train artificial neural network models for both forward prediction and inverse design. The forward model predicts the effective properties of a given structure, while the inverse model generates a structure design for the specified target properties. We validate the performance of these models by several examples such as optimizing the isotropic auxetic properties. The data-driven surrogate models greatly facilitate the practical application of these novel lattice structures for various structural and/or functional purposes. |
| Zusätzliche Informationen: | Artikel-ID: 108900 |
| Fachbereich(e)/-gebiet(e): | 11 Fachbereich Material- und Geowissenschaften 11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft 11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft > Fachgebiet Mechanik Funktionaler Materialien Zentrale Einrichtungen Zentrale Einrichtungen > Hochschulrechenzentrum (HRZ) Zentrale Einrichtungen > Hochschulrechenzentrum (HRZ) > Hochleistungsrechner |
| Hinterlegungsdatum: | 10 Jan 2024 06:18 |
| Letzte Änderung: | 26 Jan 2024 09:21 |
| PPN: | 514569174 |
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