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2021 | OriginalPaper | Chapter

Vision: Digitale Zwillinge für die Additive Fertigung

Authors: Henning Wessels, Peter Wriggers

Published in: Konstruktion für die Additive Fertigung 2020

Publisher: Springer Berlin Heidelberg

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Zusammenfassung

Die Simulation komplexer multi-physikalischer Prozesse wie in der Additiven Fertigung ist mit herkömmlichen numerischen Methoden extrem anspruchsvoll und zeitaufwändig. In der Prozessentwicklung werden daher oftmals nur vereinfachte analytische oder empirische Modelle verwendet. Durch die Fortschritte im Maschinellen Lernen können verlässliche empirische Ansätze zunehmend auch aus großen Datenmengen – Big Data – gewonnen werden. Die Generierung von Big Data in der Additiven Fertigung erfordert jedoch wiederum komplexe, teure Sensorik. Bei hohen Geschwindigkeiten und thermischen Gradienten ist es manchmal aber auch mit der besten Sensorik schlicht nicht möglich, bestimmte Daten verlässlich zu generieren.
Es konnte bereits gezeigt werden, dass neuronale Netze auch ohne Big Data und nur anhand von ohnehin vorliegenden Daten, den Anfangs- und Randbedingungen, trainiert werden können. Aktuelle Forschung beschäftigt sich daher mit der Fragestellung, inwieweit die Simulation mit neuronalen Netzen einerseits und Daten-getriebene Ansätze andererseits sinnvoll kombiniert werden können. Ultimatives Ziel ist dabei die Erzeugung digitaler Zwillinge für die additive Fertigung.
Footnotes
1
Auszüge dieses Abschnitts wurden bereits in englischer Sprache veröffentlicht [4, 5].
 
2
Auszüge dieses Abschnitts wurden bereits in englischer Sprache veröffentlicht [4, 5].
 
3
Auszüge dieses Abschnitts wurden bereits in englischer Sprache veröffentlicht [68].
 
4
Auszüge dieses Abschnitts wurden bereits in englischer Sprache veröffentlicht [68].
 
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Metadata
Title
Vision: Digitale Zwillinge für die Additive Fertigung
Authors
Henning Wessels
Peter Wriggers
Copyright Year
2021
Publisher
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-662-63030-3_5

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