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Title:

Prediction of material toughness using ensemble learning and data augmentation

Document type:
Zeitschriftenaufsatz
Author(s):
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...     »
Keywords:
Material toughness, regression, ensemble methods, data augmentation, additive manufacturing
Journal title:
Philosophical Magazine Letters
Year:
2024
Journal volume:
104
Journal issue:
1
Fulltext / DOI:
doi:10.1080/09500839.2024.2372497
WWW:
Prediction of material toughness using ensemble learning and data augmentation
Publisher:
Informa UK Limited
E-ISSN:
0950-08391362-3036
Date of publication:
15.07.2024
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