An enhanced Dendritic Neural Algorithm to predict the wear behavior of alumina coated silver reinforced copper nanocomposites

A.M. Sadoun; I.M.R. Najjar; A. Fathy; Mohamed Abd Elaziz; Mohammed A.A. Al-qaness; A.W. Abdallah; M. Elmahdy

Alexandria Engineering Journal (Feb 2023)

An enhanced Dendritic Neural Algorithm to predict the wear behavior of alumina coated silver reinforced copper nanocomposites

A.M. Sadoun,
I.M.R. Najjar,
A. Fathy,
Mohamed Abd Elaziz,
Mohammed A.A. Al-qaness,
A.W. Abdallah,
M. Elmahdy

Affiliations

A.M. Sadoun: Mechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah, Saudi Arabia; Department of Mechanical Design and Production Engineering, Faculty of Engineering, Zagazig University, P.O. Box 44519, Egypt; Corresponding author at: Mechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah, Saudi Arabia.
I.M.R. Najjar: Mechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah, Saudi Arabia
A. Fathy: Department of Mechanical Design and Production Engineering, Faculty of Engineering, Zagazig University, P.O. Box 44519, Egypt; Mechanical Department, Higher Technological Institute, Tenth of Ramadan City, Egypt
Mohamed Abd Elaziz: Faculty of Computer Science & Engineering, Galala University, Suze 43511, Egypt; Artificial Intelligence Science Program, Faculty of Science & Engineering, Galala University, Suze 43511, Egypt; Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
Mohammed A.A. Al-qaness: State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
A.W. Abdallah: Department of Mechanical Design and Production Engineering, Faculty of Engineering, Zagazig University, P.O. Box 44519, Egypt
M. Elmahdy: Mechanical Department, Higher Technological Institute, Tenth of Ramadan City, Egypt

Journal volume & issue: Vol. 65
pp. 809 – 823

Abstract

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Due to the lack of analytical solutions for the wear rates prediction of nanocomposites, we present a modified machine learning method named Dendritic Neural (DN) to predict the wear performance of copper-alumina (Cu-Al2O3) nanocomposites that have large applicability in electronics. This modification aims at determining the optimal weights of DN since they have largest influence on its performance. To achieve this improvement a new meta-heuristic technique named Artificial Hummingbird Algorithm (AHA) was used. The modified model was applied to predict the wear rates and coefficient of friction of Cu-Al2O3 nanocomposites that was developed in this study. Electroless coating of Al2O3 nanoparticles with silver (Ag) was performed to improve the wettability followed by ball milling and compaction to consolidate the composites. The microstructural, mechanical and wear properties of the produced composites with different Al2O3 content were characterized. The wear rates and coefficient of friction were evaluated using sliding wear test at different load and speeds. The developed model using AHA algorithm showed excellent predictability of the wear rate and coefficient of friction for Cu-Al2O3 nanocomposites with reinforcement content up to 10%.

Published in Alexandria Engineering Journal

ISSN: 1110-0168 (Print); 2090-2670 (Online)
Publisher: Elsevier
Country of publisher: Egypt
LCC subjects: Technology: Engineering (General). Civil engineering (General)
Website: http://www.journals.elsevier.com/alexandria-engineering-journal/

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