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Prediction of abrasive wear rate of in situ Cu–Al2O3 nanocomposite using artificial neural networks
Faculty
Engineering
Year:
2011
Type of Publication:
ZU Hosted
Pages:
Authors:
Adel Fathy Meselhy Ibrahiem
Staff Zu Site
Abstract In Staff Site
Journal:
Int J Adv Manuf Technol Springer-Verlag London Limited
Volume:
Keywords :
Prediction , abrasive wear rate , , situ Cu–Al2O3 nanocomposite
Abstract:
In this work, artificial neural networks (ANNs) technique was used in the prediction of abrasive wear rate of Cu–Al2O3 nanocomposite materials. The abrasive wear rates obtained from series of wear tests were used in the formation of the
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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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Mohamed Ali Elsayed Mohamed Agwa , "Effective Damage Indicator for Aluminum Alloy (AA6082Zr) Processed by ECAE", The American Society of Mechanical Engineers, 2018
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