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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Casati, R. |
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| Kočí, Jan | Prague |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Ali, M. A. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Joshi, Ravinder Singh
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Publications (3/3 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
- 2022Effect of REOs on tribological behavior of aluminum hybrid composites using ANNcitations
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Effect of REOs on tribological behavior of aluminum hybrid composites using ANN
Abstract
Current paper is to explore the effect of cerium oxide as REOs on tribological properties of aluminium hybrid composites with different composition of reinforcements like SiC, Al2O3 and CeO2. For this motive composites had been synthesized by varying SiC/ Al2O3 from 2.5 wt% to 7.5 wt% with equal proportion and CeO2 from 0.5 wt% to 2.5 wt% in Al-6061 matrix. The addition of cerium oxide with contents of 0.5 wt% to 2.5 wt% into the aluminium composites leads to the formation of intermetallic phase (Al4Ce3) results in improved wear rate up to 87.28%. To predict the effect of incorporating REOs reinforcements on the tribological behaviour of hybrid composites, experimental data of wear tests are used to create 3D models named Levenberg-Marquardt Algorithm (LMA) neural networks. The consequences show that the LMA- neural networks models have a high level of accuracy in the prediction of tribological properties for REOs reinforced-aluminium hybrid composites.Keywords: Rare Earth Oxides; Levenberg-Marquardt Algorithm; Specific wear rate; Microstructure; grain boundary.