| 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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Verma, Akarsh
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Publications (9/9 displayed)
- 2024Effect of Sintering Temperature on the Physical and Mechanical Characteristics of Fabricated ZrO2–Cr–Ni–Ce–Y Composite
- 2024Mechanical Characterization and Water Absorption Behavior of Waste Coconut Leaf Stalk Fiber Reinforced Hybrid Polymer Composite: Impact of Chemical Treatmentcitations
- 2024Fabrication of raw and chemically treated biodegradable Luffa aegyptica fruit fibre-based hybrid epoxy composite: a mechanical and morphological investigationcitations
- 2024Wear behaviour of aluminium-based hybrid composites processed by equal channel angular pressingcitations
- 2024Artificial neural networks for predicting mechanical properties of Al2219-B<sub>4</sub>C-Gr composites with multireinforcementscitations
- 2023Impact of graphite particle surface modification on the strengthening of cross-linked polyvinyl alcohol composites: A comprehensive investigationcitations
- 2021Fabrication and Experimental Testing of Hybrid Composite Material Having Biodegradable Bagasse Fiber in a Modified Epoxy Resin: Evaluation of Mechanical and Morphological Behaviorcitations
- 2018Experimental Analysis on Carbon Residuum Transformed Epoxy Resin: Chicken Feather Fiber Hybrid Compositecitations
- 2017Atomistic modeling of graphene/hexagonal boron nitride polymer nanocomposites: a reviewcitations
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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>