| People | Locations | Statistics |
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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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Nagaraju, Sharath Ballupete
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Publications (5/5 displayed)
- 2024Enhancing wear resistance, mechanical properties of composite materials through sisal and glass fiber reinforcement with epoxy resin and graphite fillercitations
- 2024Mechanical Characterization and Water Absorption Behavior of Waste Coconut Leaf Stalk Fiber Reinforced Hybrid Polymer Composite: Impact of Chemical Treatmentcitations
- 2024Artificial neural networks for predicting mechanical properties of Al2219-B<sub>4</sub>C-Gr composites with multireinforcementscitations
- 2023Advancing the Performance of Ceramic - Reinforced Aluminum Hybrid Composites: A Comprehensive Review and Future Perspectivescitations
- 2022Effect of B4C/Gr on Hardness and Wear Behavior of Al2618 Based Hybrid Composites through Taguchi and Artificial Neural Network Analysiscitations
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article
Artificial neural networks for predicting mechanical properties of Al2219-B<sub>4</sub>C-Gr composites with multireinforcements
Abstract
<jats:p> Artificial neural networks (ANNs) have gained prominence as a reliable model for clustering, grouping, and analysis in various domains. In recent times, machine learning (ML) models such as ANNs have proved to be on par with traditional regression and statistical models in terms of performance and usability. This study focuses on the fabrication of multicomponents-reinforced composites (Boron carbide (B<jats:sub>4</jats:sub>C) and Graphite (Gr)) using the stir casting technique. The addition of Magnesium to the melt enhances the wettability of B<jats:sub>4</jats:sub>C and Gr particles within the matrix. The microstructure and mechanical properties of the resulting Al-Mg-metal matrix composites (MMCs) are analyzed. Scanning electron micrographs reveal that B<jats:sub>4</jats:sub>C and Gr particles were uniformly dispersed in the matrix. X-Ray diffraction analysis confirmed the dispersion of the strengthening. The mechanical properties, including hardness, tensile, compressive, and impact strength, increased with the increase in B<jats:sub>4</jats:sub>C and Gr wt.%. As the percentage of B<jats:sub>4</jats:sub>C and Gr reinforcement wt.% increased, the load on the matrix reduced and its load-bearing capacity improved. The strain field generation rate also increased with an increase in B<jats:sub>4</jats:sub>C and Gr in the matrix, resulting in enhanced mechanical properties. The ANN analysis further confirmed that B<jats:sub>4</jats:sub>C was the more significant contributor to the mechanical properties of the composites. </jats:p>