Kannapinn, Maximilian ; Roth, Fabian J. ; Weeger, Oliver (2024)
Digital twin inference from multi-physical simulation data of DED additive manufacturing processes with neural ODEs.
In: ArXiv. Computational Engineering, Finance, and Science
doi: 10.48550/arXiv.2412.03295
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
A digital twin is a virtual representation that accurately replicates its physical counterpart, fostering bi-directional real-time data exchange throughout the entire process lifecycle. For Laser Directed Energy Deposition of Wire (DED-LB/w) additive manufacturing processes, digital twins may help to control the residual stress design in build parts. This study focuses on providing faster-than-real-time and highly accurate surrogate models for the formation of residual stresses by employing neural ordinary differential equations. The approach enables accurate prediction of temperatures and altered structural properties like stress tensor components. The developed surrogates can ultimately facilitate on-the-fly re-optimization of the ongoing manufacturing process to achieve desired structural outcomes. Consequently, this building block contributes significantly to realizing digital twins and the first-time-right paradigm in additive manufacturing.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Kannapinn, Maximilian ; Roth, Fabian J. ; Weeger, Oliver |
| Art des Eintrags: | Bibliographie |
| Titel: | Digital twin inference from multi-physical simulation data of DED additive manufacturing processes with neural ODEs |
| Sprache: | Englisch |
| Publikationsjahr: | 4 Dezember 2024 |
| Verlag: | Cornell University |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | ArXiv. Computational Engineering, Finance, and Science |
| Kollation: | 22 Seiten |
| DOI: | 10.48550/arXiv.2412.03295 |
| URL / URN: | https://arxiv.org/abs/2412.03295 |
| Kurzbeschreibung (Abstract): | A digital twin is a virtual representation that accurately replicates its physical counterpart, fostering bi-directional real-time data exchange throughout the entire process lifecycle. For Laser Directed Energy Deposition of Wire (DED-LB/w) additive manufacturing processes, digital twins may help to control the residual stress design in build parts. This study focuses on providing faster-than-real-time and highly accurate surrogate models for the formation of residual stresses by employing neural ordinary differential equations. The approach enables accurate prediction of temperatures and altered structural properties like stress tensor components. The developed surrogates can ultimately facilitate on-the-fly re-optimization of the ongoing manufacturing process to achieve desired structural outcomes. Consequently, this building block contributes significantly to realizing digital twins and the first-time-right paradigm in additive manufacturing. |
| ID-Nummer: | Artikel-ID: 2412.03295 |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Fachgebiet Cyber-Physische Simulation (CPS) |
| Hinterlegungsdatum: | 06 Jan 2025 06:53 |
| Letzte Änderung: | 06 Jan 2025 06:53 |
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