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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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Hamilton, Andrew R.
University of Southampton
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (16/16 displayed)
- 2024Interfacial characteristics of multi-material SS316L/IN718 fabricated by laser powder bed fusion and processed by high-pressure torsion
- 2023Fatigue crack initiation and growth behavior within varying notch geometries in the low-cycle fatigue regime for FV566 turbine blade materialcitations
- 2023Fatigue crack initiation and growth behavior within varying notch geometries in the low-cycle fatigue regime for FV566 turbine blade materialcitations
- 2023Hydrated behavior of multilayer polyelectrolyte-nanoclay coatings on porous materials and demonstration of shape memory effectcitations
- 2023Hydrated behavior of multilayer polyelectrolyte-nanoclay coatings on porous materials and demonstration of shape memory effectcitations
- 2023Interfacial characteristics of austenitic 316L and martensitic 15-5PH stainless steels joined by laser powder bed fusioncitations
- 2022Effects of rescanning parameters on densification and microstructural refinement of 316L stainless steel fabricated by laser powder bed fusioncitations
- 2021Fatigue crack initiation and growth behavior in a notch with periodic overloads in the low-cycle fatigue regime of FV566 ex-service steam turbine blade materialcitations
- 2021Fatigue crack initiation and growth behavior in a notch with periodic overloads in the low-cycle fatigue regime of FV566 ex-service steam turbine blade materialcitations
- 2019Behaviour of 3D printed PLA and PLA-PHA in marine environmentscitations
- 2016Porous materials with tunable structure and mechanical properties via templated layer-by-layer assemblycitations
- 2016Optimization and Prediction of Mechanical and Thermal Properties of Graphene/LLDPE Nanocomposites by Using Artificial Neural Networkscitations
- 2015Melt Processing and Properties of Polyamide 6/Graphene Nanoplatelet Compositescitations
- 2015Characterisation of melt processed nanocomposites of Polyamide 6 subjected to uniaxial-drawing
- 2015Customization of mechanical properties and porosity of bone tissue scaffold materials via Layer-by-Layer assembly of polymer-nanocomposite coatingscitations
- 2013Evaluation of the anisotropic mechanical properties of reinforced polyurethane foamscitations
Places of action
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article
Optimization and Prediction of Mechanical and Thermal Properties of Graphene/LLDPE Nanocomposites by Using Artificial Neural Networks
Abstract
The focus of this work is to develop the knowledge of prediction of the physical and chemical properties of processed linear low density polyethylene (LLDPE)/graphene nanoplatelets composites. Composites made from LLDPE reinforced with 1, 2, 4, 6, 8, and 10 wt% grade C graphene nanoplatelets (C-GNP) were processed in a twin screw extruder with three different screw speeds and feeder speeds (50, 100, and 150 rpm). These applied conditions are used to optimize the following properties: thermal conductivity, crystallization temperature, degradation temperature, and tensile strength while prediction of these properties was done through artificial neural network (ANN). The three first properties increased with increase in both screw speed and C-GNP content. The tensile strength reached a maximum value at 4 wt% C-GNP and a speed of 150 rpm as this represented the optimum condition for the stress transfer through the amorphous chains of the matrix to the C-GNP. ANN can be confidently used as a tool to predict the above material properties before investing in development programs and actual manufacturing, thus significantly saving money, time, and effort.