| 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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Abdo, Hany S.
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (18/18 displayed)
- 2023Investigating the Mechanical Properties of Annealed 3D-Printed PLA–Date Pits Compositecitations
- 2023Adaptive Neuro-Fuzzy-Based Models for Predicting the Tribological Properties of 3D-Printed PLA Green Composites Used for Biomedical Applicationscitations
- 2023Investigation of the Mechanical and Tribological Behavior of Epoxy-Based Hybrid Compositecitations
- 2023Hydroxyapatite–Clay Composite for Bone Tissue Engineering: Effective Utilization of Prawn Exoskeleton Biowastecitations
- 2023Ecofriendly Biochar as a Low-Cost Solid Lubricating Filler for LDPE Sustainable Biocomposites: Thermal, Mechanical, and Tribological Characterizationcitations
- 2023Casting light on the tribological properties of paraffin-based HDPE enriched with graphene nano-additives: an experimental investigationcitations
- 2023Effect of Synthesized Titanium Dioxide Nanofibers Weight Fraction on the Tribological Characteristics of Magnesium Nanocomposites Used in Biomedical Applicationscitations
- 2022Mechanical Alloying of Ball-Milled Cu–Ti–B Elemental Powder with the In Situ Formation of Titanium Diboridecitations
- 2022Comparative Study into Microstructural and Mechanical Characterization of HVOF-WC-Based Coatingscitations
- 2022Study on the Microstructure of Vanadium-Modified Tungsten High-Speed Steel-Coded SAE-AISI T1 Steelcitations
- 2021Electrochemical Corrosion Behavior of Laser Welded 2205 Duplex Stainless-Steel in Artificial Seawater Environment under Different Acidity and Alkalinity Conditionscitations
- 2021Mitigating Corrosion Effects of Ti-48Al-2Cr-2Nb Alloy Fabricated via Electron Beam Melting (EBM) Technique by Regulating the Immersion Conditionscitations
- 2021Electrochemical Behavior of Inductively Sintered Al/TiO2 Nanocomposites Reinforced by Electrospun Ceramic Nanofiberscitations
- 2020The Cyclic Oxidation and Hardness Characteristics of Thermally Exposed Titanium Prepared by Inductive Sintering-Assisted Powder Metallurgycitations
- 2020Influence of Extrusion Temperature on the Corrosion Behavior in Sodium Chloride Solution of Solid State Recycled Aluminum Alloy 6061 Chipscitations
- 2020Regulating Mechanical Properties of Al/SiC by Utilizing Different Ball Milling Speedscitations
- 2017Effect of Nickel Content on the Corrosion Resistance of Iron-Nickel Alloys in Concentrated Hydrochloric Acid Pickling Solutionscitations
- 2015Corrosion inhibition of cast iron in Arabian Gulf seawater by two different ionic liquidscitations
Places of action
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article
Adaptive Neuro-Fuzzy-Based Models for Predicting the Tribological Properties of 3D-Printed PLA Green Composites Used for Biomedical Applications
Abstract
<jats:p>Tribological performance is a critical aspect of materials used in biomedical applications, as it can directly impact the comfort and functionality of devices for individuals with disabilities. Polylactic Acid (PLA) is a widely used 3D-printed material in this field, but its mechanical and tribological properties can be limiting. This study focuses on the development of an artificial intelligence model using ANFIS to predict the wear volume of PLA composites under various conditions. The model was built on data gathered from tribological experiments involving PLA green composites with different weight fractions of date particles. These samples were annealed for different durations to eliminate residual stresses from 3D printing and then subjected to tribological tests under varying normal loads and sliding distances. Mechanical properties and finite element models were also analyzed to better understand the tribological results and evaluate the load-carrying capacity of the PLA composites. The ANFIS model demonstrated excellent compatibility and robustness in predicting wear volume, with an average percentage error of less than 0.01% compared to experimental results. This study highlights the potential of heat-treated PLA green composites for improved tribological performance in biomedical applications.</jats:p>