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Titel:

Prediction of material toughness using ensemble learning and data augmentation

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Smyrnov, Mykyta; Funcke, Florian; Kabliman, Evgeniya
Abstract:
The present work investigates the impact resistance of metallic parts produced using Laser Powder Bed Fusion and the possibility of its prediction using machine learning algorithms. The challenge lies in finding optimal process parameters before printing based on the existing data. Economic constraints often result in the availability of only a limited amount of data for predictive purposes. In this work, around one hundred data points from Charpy impact tests on AlSi10Mg0.5 were used to analyse...     »
Stichworte:
Material toughness, regression, ensemble methods, data augmentation, additive manufacturing
Zeitschriftentitel:
Philosophical Magazine Letters
Jahr:
2024
Band / Volume:
104
Heft / Issue:
1
Volltext / DOI:
doi:10.1080/09500839.2024.2372497
WWW:
Prediction of material toughness using ensemble learning and data augmentation
Verlag / Institution:
Informa UK Limited
E-ISSN:
0950-08391362-3036
Publikationsdatum:
15.07.2024
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