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Utilization of Improved Machine Learning Method Based on Artificial Hummingbird Algorithm to Predict the Tribological Behavior of Cu-Al2O3 Nanocomposites Synthesized by In Situ Method
Faculty
Engineering
Year:
2022
Type of Publication:
ZU Hosted
Pages:
Authors:
Adel Fathy Meselhy Ibrahiem
Staff Zu Site
Abstract In Staff Site
Journal:
Mathematics MDPI
Volume:
Keywords :
Utilization , Improved Machine Learning Method Based
Abstract:
This paper presents a machine learning model to predict the effect of Al2O3 nanoparticles content on the wear rates in Cu-Al2O3 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 Al2O3 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 Al2O3 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% Al2O3 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 Al2O3 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 R2.
Author Related Publications
Adel Fathy Meselhy Ibrahiem, "Effect of matrix/reinforcement particle size ratio (PSR) on the mechanical properties of extruded Al–SiC composites", Springer, 2014
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Adel Fathy Meselhy Ibrahiem, "The effect of Mg add on morphology and mechanical properties of Al–xMg/10Al2O3 nanocomposite produced by mechanical alloying", Elsevier, 2014
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Adel Fathy Meselhy Ibrahiem, "Effect of Iron Addition on the Microstructure, Mechanical and Magnetic Properties of Al-Matrix Composite Produced by Powder Metallurgy Route", Elsevier, 2014
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Adel Fathy Meselhy Ibrahiem, "Compressive and wear resistance of nanometric alumina reinforced copper matrix composites", SciVerse ScienceDirect, 2011
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Adel Fathy Meselhy Ibrahiem, "Prediction of abrasive wear rate of in situ Cu–Al2O3 nanocomposite using artificial neural networks", Springer-Verlag London Limited, 2011
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Department Related Publications
Ashraf Abdelfattah Ali Hassanein, "Wet Electrospun CuNP/Carbon Nano Fibril Composites: Potential Application for Micro Surface Mounted Components", Journal of Applied NanoScience, 2012; 2: 55-61, 2011
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Mohammed Abdelmoniem Mohamed Eltaher , "Behavior of a viscoelastic composite plates under transient load", www.springerlink.com/content/1738-494x, 2011
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Tamer Ali Abdella Sebaee, "Characterization of crack propagation in mode I delamination of multidirectional CFRP laminates", ScienceDirect, 2012
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Adel Fathy Meselhy Ibrahiem, "Thermal expansion and thermal conductivity characteristics of Cu–Al2O3 nanocomposites", SciVerse ScienceDirect, 2012
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Ahmed Abou ElWafa Megahed Abou ElWafa Elbasyouni, "Prediction of Sliding Wear Rate of Extruded Al–SiC Composite using Artificial Neural Networks", Assiut University - Faculty of Engineering, 2011
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