Prediction of Tribological Properties of Alumina-Coated, Silver-Reinforced Copper Nanocomposites Using Long Short-Term Model Combined with Golden Jackal Optimization
In this paper, we present a newly modified machine learning model that employs a long short-term memory (LSTM) neural network model with the golden jackal optimization (GJO) algorithm to predict the tribological performance of Cu–Al<sub>2</sub>O<sub>3</sub> nanocomposites. Th...
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MDPI AG
2022-10-01
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| Series: | Lubricants |
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| Online Access: | https://www.mdpi.com/2075-4442/10/11/277 |
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| author | Ismail R. Najjar Ayman M. Sadoun Adel Fathy Ahmed W. Abdallah Mohamed Abd Elaziz Marwa Elmahdy |
| author_facet | Ismail R. Najjar Ayman M. Sadoun Adel Fathy Ahmed W. Abdallah Mohamed Abd Elaziz Marwa Elmahdy |
| author_sort | Ismail R. Najjar |
| collection | DOAJ |
| description | In this paper, we present a newly modified machine learning model that employs a long short-term memory (LSTM) neural network model with the golden jackal optimization (GJO) algorithm to predict the tribological performance of Cu–Al<sub>2</sub>O<sub>3</sub> nanocomposites. The modified model was applied to predict the wear rates and coefficient of friction of Cu–Al<sub>2</sub>O<sub>3</sub> nanocomposites that were developed in this study. Electroless coating of Al<sub>2</sub>O<sub>3</sub> nanoparticles with 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 Al<sub>2</sub>O<sub>3</sub> content were characterized. The wear rates and coefficient of friction were evaluated using sliding wear tests at different loads and speeds. From a materials point of view, the manufactured composites with 10% Al<sub>2</sub>O<sub>3</sub> content showed huge enhancement in hardness and wear rates compared to pure copper, reaching 170% and 65%, respectively. The improvement of the properties was due to the excellent mechanical properties of Al<sub>2</sub>O<sub>3</sub>, grain refinement, and dislocation movement impedance. The developed model using the LSTM-GJO algorithm showed excellent predictability of the wear rate and coefficient of friction for all the considered composites. |
| first_indexed | 2024-03-09T18:54:28Z |
| format | Article |
| id | doaj.art-132bbc7570824ee3952ec08e7299f480 |
| institution | Directory Open Access Journal |
| issn | 2075-4442 |
| language | English |
| last_indexed | 2024-03-09T18:54:28Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Lubricants |
| spelling | doaj.art-132bbc7570824ee3952ec08e7299f4802023-11-24T05:31:50ZengMDPI AGLubricants2075-44422022-10-01101127710.3390/lubricants10110277Prediction of Tribological Properties of Alumina-Coated, Silver-Reinforced Copper Nanocomposites Using Long Short-Term Model Combined with Golden Jackal OptimizationIsmail R. Najjar0Ayman M. Sadoun1Adel Fathy2Ahmed W. Abdallah3Mohamed Abd Elaziz4Marwa Elmahdy5Mechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah P.O. Box 80204, Saudi ArabiaMechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah P.O. Box 80204, Saudi ArabiaDepartment of Mechanical Design and Production Engineering, Faculty of Engineering, Zagazig University, Zagazig 44519, EgyptDepartment of Mechanical Design and Production Engineering, Faculty of Engineering, Zagazig University, Zagazig 44519, EgyptFaculty of Computer Science & Engineering, Galala University, Suze 43511, EgyptMechanical Department, Higher Technological Institute, Tenth of Ramadan City 44629, EgyptIn this paper, we present a newly modified machine learning model that employs a long short-term memory (LSTM) neural network model with the golden jackal optimization (GJO) algorithm to predict the tribological performance of Cu–Al<sub>2</sub>O<sub>3</sub> nanocomposites. The modified model was applied to predict the wear rates and coefficient of friction of Cu–Al<sub>2</sub>O<sub>3</sub> nanocomposites that were developed in this study. Electroless coating of Al<sub>2</sub>O<sub>3</sub> nanoparticles with 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 Al<sub>2</sub>O<sub>3</sub> content were characterized. The wear rates and coefficient of friction were evaluated using sliding wear tests at different loads and speeds. From a materials point of view, the manufactured composites with 10% Al<sub>2</sub>O<sub>3</sub> content showed huge enhancement in hardness and wear rates compared to pure copper, reaching 170% and 65%, respectively. The improvement of the properties was due to the excellent mechanical properties of Al<sub>2</sub>O<sub>3</sub>, grain refinement, and dislocation movement impedance. The developed model using the LSTM-GJO algorithm showed excellent predictability of the wear rate and coefficient of friction for all the considered composites.https://www.mdpi.com/2075-4442/10/11/277long short-term modelgolden jackal optimizationCu–Al<sub>2</sub>O<sub>3</sub> nanocompositestribological properties |
| spellingShingle | Ismail R. Najjar Ayman M. Sadoun Adel Fathy Ahmed W. Abdallah Mohamed Abd Elaziz Marwa Elmahdy Prediction of Tribological Properties of Alumina-Coated, Silver-Reinforced Copper Nanocomposites Using Long Short-Term Model Combined with Golden Jackal Optimization Lubricants long short-term model golden jackal optimization Cu–Al<sub>2</sub>O<sub>3</sub> nanocomposites tribological properties |
| title | Prediction of Tribological Properties of Alumina-Coated, Silver-Reinforced Copper Nanocomposites Using Long Short-Term Model Combined with Golden Jackal Optimization |
| title_full | Prediction of Tribological Properties of Alumina-Coated, Silver-Reinforced Copper Nanocomposites Using Long Short-Term Model Combined with Golden Jackal Optimization |
| title_fullStr | Prediction of Tribological Properties of Alumina-Coated, Silver-Reinforced Copper Nanocomposites Using Long Short-Term Model Combined with Golden Jackal Optimization |
| title_full_unstemmed | Prediction of Tribological Properties of Alumina-Coated, Silver-Reinforced Copper Nanocomposites Using Long Short-Term Model Combined with Golden Jackal Optimization |
| title_short | Prediction of Tribological Properties of Alumina-Coated, Silver-Reinforced Copper Nanocomposites Using Long Short-Term Model Combined with Golden Jackal Optimization |
| title_sort | prediction of tribological properties of alumina coated silver reinforced copper nanocomposites using long short term model combined with golden jackal optimization |
| topic | long short-term model golden jackal optimization Cu–Al<sub>2</sub>O<sub>3</sub> nanocomposites tribological properties |
| url | https://www.mdpi.com/2075-4442/10/11/277 |
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