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Data-driven density prediction of AlSi10Mg parts produced by laser powder bed fusion using machine learning and finite element simulation
Citation Link: https://doi.org/10.15480/882.8765
Publikationstyp
Journal Article
Date Issued
2023-09-26
Sprache
English
TORE-DOI
Journal
Journal of Laser Applications
Volume
35
Issue
4
Article Number
042023
Citation
Journal of Laser Applications 35 (4): 042023 (2023-09-26)
Publisher DOI
Scopus ID
Powder bed fusion of metals using laser beam (PBF-LB/M) is a commonly used additive manufacturing process for the production of high-performance metal parts. AlSi10Mg is a widely used material in PBF-LB/M due to its excellent mechanical and thermal properties. However, the part quality of AlSi10Mg parts produced using PBF-LB/M can vary significantly depending on the process parameters. This study investigates the use of machine learning (ML) algorithms for the prediction of the resulting part density of AlSi10Mg parts produced using PBF-LB/M. An empirical data set of PBF-LB/M process parameters and resulting part densities is used to train ML models. Furthermore, a methodology is developed to allow density predictions based on simulated meltpool dimensions for different process parameters. This approach uses finite element simulations to calculate the meltpool dimensions, which are then used as input parameters for the ML models. The accuracy of this methodology is evaluated by comparing the predicted densities with experimental measurements. The results show that ML models can accurately predict the part density of AlSi10Mg parts produced using PBF-LB/M. Moreover, the methodology based on simulated meltpool dimensions can provide accurate predictions while significantly reducing the experimental effort needed in process development in PBF-LB/M. This study provides insights into the development of data-driven approaches for the optimization of PBF-LB/M process parameters and the prediction of part properties.
Subjects
additive manufacturing
AlSi10Mg
density prediction
finite element method
machine learning
PBF-LB/M
DDC Class
621: Applied Physics
Publication version
publishedVersion
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042023_1_7.0001141.pdf
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2.95 MB
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