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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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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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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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Kumar, Vinod
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (17/17 displayed)
- 2024Bioactivity and corrosion analysis of thermally sprayed hydroxyapatite based coatings
- 2024MIL-101(Cr)/Aminoclay Nanocomposites for Conversion of CO2 into Cyclic Carbonatescitations
- 2024Cellulose‐based smart materials: Novel synthesis techniques, properties, and applications in energy storage and conversion devicescitations
- 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
- 2023Structural, morphological, optical and biomedical applications of Berberis aristata mediated ZnO and Ag-ZnO nanoparticlescitations
- 2023Organic sensing element approach in electrochemical sensor for automated and accurate pesticides detectioncitations
- 2023Sustainable utilization and valorization of potato waste: state of the art, challenges, and perspectivescitations
- 2022On-Sun Testing of a High-Temperature Solar Receiver’s Flux Distributioncitations
- 2022Effect of REOs on tribological behavior of aluminum hybrid composites using ANNcitations
- 2021A CNN With Deep Learning for Non-Equilibrium Characterization of Al-Sm Melt Infusion Into a B4C Packed Bed
- 2019Uncertainty Quantification of Molten Hafnium Infusion Into a B4C Packed-Bed
- 2019Microstructure and magnetic behavior of FeCoNi(Mn-Si)x (x = 0.5, 0.75, 1.0) high-entropy alloyscitations
- 2018Utilization of Machine Learning to Predict the Surface Tension of Metals and Alloys
- 2018Predicting the Depth of Penetration of Molten Metal Into a Pore Network Using TensorFlow
- 2015Optical and Structural Study of Polyaniline/Polystyrene Composite Filmscitations
- 2012Simulation of Cooling Rate of Gray Cast Iron Casting in a Sand Mold and its Experimental Validationcitations
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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>