An enhanced Dendritic Neural Algorithm to predict the wear behavior of alumina coated silver reinforced copper nanocomposites
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 modificati...
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| Published in | Alexandria engineering journal Vol. 65; pp. 809 - 823 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
15.02.2023
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1110-0168 2090-2670 |
| DOI | 10.1016/j.aej.2022.09.036 |
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| Abstract | 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%. |
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| AbstractList | 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%. |
| Author | Abd Elaziz, Mohamed Najjar, I.M.R. Elmahdy, M. Fathy, A. Al-qaness, Mohammed A.A. Abdallah, A.W. Sadoun, A.M. |
| Author_xml | – sequence: 1 givenname: A.M. surname: Sadoun fullname: Sadoun, A.M. email: Assadoun@kau.edu.sa organization: Mechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah, Saudi Arabia – sequence: 2 givenname: I.M.R. surname: Najjar fullname: Najjar, I.M.R. organization: Mechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah, Saudi Arabia – sequence: 3 givenname: A. surname: Fathy fullname: Fathy, A. organization: Department of Mechanical Design and Production Engineering, Faculty of Engineering, Zagazig University, P.O. Box 44519, Egypt – sequence: 4 givenname: Mohamed surname: Abd Elaziz fullname: Abd Elaziz, Mohamed organization: Faculty of Computer Science & Engineering, Galala University, Suze 43511, Egypt – sequence: 5 givenname: Mohammed A.A. surname: Al-qaness fullname: Al-qaness, Mohammed A.A. organization: State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China – sequence: 6 givenname: A.W. surname: Abdallah fullname: Abdallah, A.W. organization: Department of Mechanical Design and Production Engineering, Faculty of Engineering, Zagazig University, P.O. Box 44519, Egypt – sequence: 7 givenname: M. surname: Elmahdy fullname: Elmahdy, M. organization: Mechanical Department, Higher Technological Institute, Tenth of Ramadan City, Egypt |
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| Keywords | Cu-Al2O3 nanocomposites Artificial Hummingbird Algorithm (AHA) Meta-heuristic Dendritic Neural |
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| SubjectTerms | Artificial Hummingbird Algorithm (AHA) Cu-Al2O3 nanocomposites Dendritic Neural Meta-heuristic |
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| Title | An enhanced Dendritic Neural Algorithm to predict the wear behavior of alumina coated silver reinforced copper nanocomposites |
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