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Title:

Advantages of Hybrid Neural Network Architectures to Enhance Prediction of Tensile Properties in Laser Powder Bed Fusion

Document type:
Zeitschriftenaufsatz
Author(s):
Funcke, Florian; Forster, Tobias; Mayr, Peter
Abstract:
The properties of AlSi10Mg produced by Laser Powder Bed Fusion (PBF-LB) are defined by a multitude of different machine and laser parameters. This multi-parameter space presents the challenge of optimizing the material properties for a given application by the sheer amount of possible parameter combinations. Characterizing this multi-parameter space empirically is limited by time and resources and thus yields an incomplete picture of the process capabilities and local optima, respectively. To im...     »
Keywords:
Additive Manufacturing, Neural Networks, Laser Powder Bed Fusion, Micrographs
Journal title:
Key Engineering Materials
Year:
2023
Journal volume:
964
Pages contribution:
65-71
Reviewed:
ja
Language:
en
Fulltext / DOI:
doi:10.4028/p-0tcamf
WWW:
https://www.scientific.net/KEM.964.65
Publisher:
Trans Tech Publications, Ltd.
E-ISSN:
1662-9795
Submitted:
31.05.2023
Accepted:
31.08.2023
Date of publication:
23.11.2023
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