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

Ayman M. Sadoun, Ismail R. Najjar, Ghazi S. Alsoruji, M. S. Abd-Elwahed, Mohamed Abd Elaziz and Adel Fathy
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Ayman M. Sadoun: Mechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah P.O. Box 80204, Saudi Arabia
Ismail R. Najjar: Mechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah P.O. Box 80204, Saudi Arabia
Ghazi S. Alsoruji: Mechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah P.O. Box 80204, Saudi Arabia
M. S. Abd-Elwahed: Mechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah P.O. Box 80204, Saudi Arabia
Mohamed Abd Elaziz: Faculty of Computer Science & Engineering, Galala University, Suze 435611, Egypt
Adel Fathy: Department of Mechanical Design and Production Engineering, Faculty of Engineering, Zagazig University, Zagazig P.O. Box 44519, Egypt

Mathematics, 2022, vol. 10, issue 8, 1-14

Abstract: This paper presents a machine learning model to predict the effect of Al 2 O 3 nanoparticles content on the wear rates in Cu-Al 2 O 3 nanocomposite prepared using in situ chemical technique. The model developed is a modification of the random vector functional link (RVFL) algorithm using artificial hummingbird algorithm (AHA). The objective of using AHA is used to find the optimal configuration of RVFL to enhance the prediction of Al 2 O 3 nanoparticles. The preparation of the composite was done using aluminum nitrate that was added to a solution containing scattered copper nitrate. After that, the powders of CuO and Al 2 O 3 were obtained, and the leftover liquid was removed using a thermal treatment at 850 °C for 1 h. The powders were consolidated using compaction and sintering processes. The microhardness of the nanocomposite with 12.5% Al 2 O 3 content is 2.03-fold times larger than the pure copper, while the wear rate of the same composite is reduced, reaching 55% lower than pure copper. These improved properties are attributed to the presence of Al 2 O 3 nanoparticles and their homogenized distributions inside the matrix. The developed RVFl-AHA model was able to predict the wear rates of all the prepared composites at different wear load and speed, with very good accuracy, reaching nearly 100% and 99.5% using training and testing, respectively, in terms of coefficient of determination R 2 .

Keywords: metal matrix nanocomposites; hardness; abrasive wear rate; artificial neural network; RVFL algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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