| 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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Ružić, Jovana
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (19/19 displayed)
- 2024Application of powder metallurgy in the production of the copper-based material
- 2024Mechanical alloying as a crucial step in the fabrication process of Cu alloys
- 2024Cost-Effective Production of CuCrZr Alloy Using Powder Metallurgy ; Isplativa proizvodnja legure CuCrZr primenom metalurgije prahacitations
- 2024Application of Machine Learning for Predicting Mechanical Properties and Designing Novel Biocompatible Titanium Alloys
- 2024Mechanical properties of mullite investigated by nanoindentationcitations
- 2024Prediction of elastic modulus, yield strength, and tensile strength in biocompatible titanium alloys
- 2023Study of the changes in mechanical properties of the copper-zirconium alloys influenced by minor boron addition
- 2023Static and kinetic friction of electroless Ni composite coatings
- 2023Interaction of ns laser with 316L-NiB stainless steel obtained by powder metallurgy – morphological effects and LIBS analysis
- 2023The effect of mechanical alloying parameters on the copper matrix composite materials
- 2023Predicting Low-Modulus Biocompatible Titanium Alloys Using Machine Learningcitations
- 2022Multiscale Modelling and Characterization of Mechanical Properties in Heat-Resistant Alloys
- 2022X-Ray analysis by Williamson-Hall and stereological analysis of mechanically alloyed Cu-Zr-B alloys
- 2021Innovative processing routes in manufacturing of metal matrix composite materialscitations
- 2020Data analytics approach to predict the hardness of copper matrix compositescitations
- 2020Effect of process parameters on the phase transformation kinetics in copper-based alloys and compositescitations
- 2019Microstructural and basic mechanical characteristics of ZA27 alloy-based nanocomposites synthesized by mechanical milling and compocastingcitations
- 2018Influence of the fabrication process of copper matrix composites on cavitation erosion resistancecitations
- 2014Prediction of hardness and electrical properties in ZrB2 particle reinforced metal matrix composites using artificial neural networkcitations
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article
Prediction of hardness and electrical properties in ZrB2 particle reinforced metal matrix composites using artificial neural network
Abstract
<jats:p>In the present study, the hardness and electrical properties of copper based composite prepared by hot pressing of mechanically alloyed powders were predicted using Artificial Neural Network (ANN) approach. Milling time (t, h), particles size of mechanically alloyed powders (d, nm), dislocation density (ρ, m-2) and compressive yield stress (σ0.2, MPa) were used as inputs. The ANN model was developed using general regression neural network (GRNN) architecture. Cu-based composites reinforced with micro and nano ZrB2 particles were consolidated via powder metallurgy processing by combining mechanical alloying and hot pressing. Analysis of the obtained results concerning hardness and electrical properties of the Cu-7 vol.% ZrB2 alloy showed that the distribution of micro and nano ZrB2 particles and the presence of agglomerates in the Cu matrix directly depend on the milling time. Also, the results show a strong influence of the milling time on hardness and electrical properties of Cu-7 vol.% ZrB2 alloy. Addition of ZrB2 particles decreases electrical conductivity of copper, but despite this fact Cu-7 vol.% ZrB2 alloy can be marked as highly conductive alloy (samples made of mechanically alloyed powders milled longer than 20 h). Experimental results of the samples have shown a consistency with the predicted results of ANN. </jats:p>