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| Naji, M. |
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| Ertürk, Emre |
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| Petrov, R. H. | Madrid |
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| Azevedo, Nuno Monteiro |
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Kumar, Pardeep
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Publications (5/5 displayed)
- 2024Corrosion inhibition analysis on cerium induced hydrophobic surface of Al-6061/SiC/Al<sub>2</sub>O<sub>3</sub> hybrid compositescitations
- 2023Analyzing the tribological and mechanical performance of Al-6061 with rare earth oxides: An experimental analysiscitations
- 2023Optimization of Dual Extrusion Fused Filament Fabrication Process Parameters for 3D Printed Nylon-Reinforced Composites: Pathway to Mobile and Transportation Revolutioncitations
- 2021Optimization of process parameters for Surface Roughness and Material Removal Rate of H13 die tool steel for wire EDM using Taguchi Techniquecitations
- 2021Parametric optimization in WEDM with H-13 hot die-hard steel using Taguchi Method
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article
Analyzing the tribological and mechanical performance of Al-6061 with rare earth oxides: An experimental analysis
Abstract
<jats:p> This article aims to evaluate the effect of ceria oxide as rare earth oxides (REOs) on the tribological properties of aluminum hybrid composites with varied concentrations of reinforcing elements such as silicon carbide, aluminum oxide, and ceria oxide. In order to accomplish this, composites were produced by varying the percentage of SiC/Al<jats:sub>2</jats:sub>O<jats:sub>3</jats:sub> in the Al-6061 matrix from 2.5 to 7.5 wt% and the quantity of CeO<jats:sub>2</jats:sub> from 0.5 to 2.5 wt%. The formation of the intermetallic phase (Al<jats:sub>4</jats:sub>Ce<jats:sub>3</jats:sub>) as a result of the integration of cerium oxide into aluminum composites at concentrations between 0.5 and 2.5 wt% results in a wear rate improvement of up to 87.28%. The objective of developing Levenberg-Marquardt algorithm (LMA) neural networks is to forecast how the tribological behavior of hybrid composites would be altered by the addition of REOs based on data acquired from wear testing. The correlation value (R) and mean square error are found to be 0.987 and 4.3424e<jats:sup>−10</jats:sup>, respectively, which is an indication of good fit for the model with high significance. The findings indicate that the LMA neural network models accurately forecast the tribological properties of REOs–aluminum hybrid composites. </jats:p>