Utilization of Improved Machine Learning Method Based on Artificial Hummingbird Algorithm to Predict the Tribological Behavior of Cu-Al<sub>2</sub>O<sub>3</sub> Nanocomposites Synthesized by In Situ Method

Utilization of Improved Machine Learning Method Based on Artificial Hummingbird Algorithm to Predict the Tribological Behavior of Cu-Al<sub>2</sub>O<sub>3</sub> Nanocomposites Synthesized by In Situ Method

This paper presents a machine learning model to predict the effect of Al<sub>2</sub>O<sub>3</sub> nanoparticles content on the wear rates in Cu-Al<sub>2</sub>O<sub>3</sub> nanocomposite prepared using in situ chemical technique. The model developed is...

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Bibliographic Details
Main Authors: Ayman M. Sadoun, Ismail R. Najjar, Ghazi S. Alsoruji, M. S. Abd-Elwahed, Mohamed Abd Elaziz, Adel Fathy
Format: Article
Language:English
Published: MDPI AG 2022-04-01
Series:Mathematics
Subjects:
metal matrix nanocomposites
hardness
abrasive wear rate
artificial neural network
RVFL algorithm
Online Access:https://www.mdpi.com/2227-7390/10/8/1266

Internet

https://www.mdpi.com/2227-7390/10/8/1266

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