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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Veröffentlicht in:	Journal of materials research and technology Jg. 33; S. 3684 - 3695
Hauptverfasser:	Jatti, Vijaykumar S., Murali Krishnan, R., Saiyathibrahim, A., Preethi, V., Priyadharshini G, Suganya, Kumar, Abhinav, Sharma, Shubham, Islam, Saiful, Kozak, Dražan, Lozanovic, Jasmina
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.11.2024 Elsevier
Schlagworte:	Gaussian process regression algorithm Laser powder bed fusion Machine learning regression algorithm MAE Prediction RMSE Specific wear rate MAE Laser powder bed fusion Prediction Gaussian process regression algorithm Machine learning regression algorithm RMSE Specific wear rate
ISSN:	2238-7854
Online-Zugang:	Volltext
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