Digital twin inference from multi-physical simulation data of DED additive manufacturing processes with neural ODEs

Autor:	Kannapinn, Maximilian, Roth, Fabian, Weeger, Oliver
Rok vydání:	2024
Předmět:	Computer Science - Computational Engineering Finance and Science Physics - Computational Physics
Druh dokumentu:	Working Paper
Popis:	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. Comment: Presented at the ICCE 2024, Darmstadt
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.03295 Zobrazit plný text záznamu View this record from Arxiv