Predicting specific wear rate of laser powder bed fusion AlSi10Mg parts at elevated temperatures using machine learning regression algorithm: Unveiling of microstructural morphology analysis

Predicting specific wear rate of laser powder bed fusion AlSi10Mg parts at elevated temperatures using machine learning regression algorithm: Unveiling of microstructural morphology analysis

Precisely predicting the Specific Wear Rate (SWR) of AlSi10Mg components produced using Laser Powder Bed Fusion (LPBF) at high temperatures, which is an essential concern in additive manufacturing. This study aims to address the gap in literature by developing accurate predictive models for SWR via...

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Bibliographic Details
Main Authors: Vijaykumar S. Jatti, R. Murali Krishnan, A. Saiyathibrahim, V. Preethi, Suganya Priyadharshini G, Abhinav Kumar, Shubham Sharma, Saiful Islam, Dražan Kozak, Jasmina Lozanovic
Format: Article
Language:English
Published: Elsevier 2024-11-01
Series:Journal of Materials Research and Technology
Subjects:
Laser powder bed fusion
Machine learning regression algorithm
Prediction
Specific wear rate
MAE
RMSE
Online Access:http://www.sciencedirect.com/science/article/pii/S223878542402249X

Internet

http://www.sciencedirect.com/science/article/pii/S223878542402249X

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