693.932 PEOPLE
| 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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Vencl, Aleksandar
University of Belgrade
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (37/37 displayed)
- 2024Effect of temperature treatments on microhardness of additively manufactured PETG
- 2024Tribological Behaviour of Hypereutectic Al-Si Composites: A Multi-Response Optimisation Approach with ANN and Taguchi Grey Methodcitations
- 2023Joining of composite materials based on Al-Si alloys by using the GMAW process
- 2023Optimization of tribological behaviour of hybrid composites based on A356 and ZA-27 alloys
- 2023Wear of A356/Al2O3 nanocomposites and optimisation of material and operating parameters
- 2023Influence of materials on the efficiency of worm gear transmission
- 2023Characterization and comparison of the carbides morphologies in the near surface region of the single- and double layer iron-based hardfaced coatings
- 2023Friction and wear properties of copper-based composites reinforced with micro- and nano-sized Al2O3 particles
- 2023Prediction of the wear characteristics of ZA-27/SiC nanocomposites using the artificial neural network
- 2023Static and kinetic friction of electroless Ni composite coatings
- 2023A review of the tribological properties of PTFE composites filled with glass, graphite, carbon or bronze reinforcement
- 2023Influence of "Valena" metal-plating additive on the friction properties of ball bearings
- 2023Optimization and prediction of aluminium composite wear using Taguchi design and artificial neural network
- 2023A review on mechanical and tribological properties of aluminium-based metal matrix nanocomposites
- 2023Comparative analysis of hybrid composites based on A356 and ZA-27 alloys regarding their tribological behaviourcitations
- 2023Influence of Al2O3 nanoparticles addition in ZA-27 alloy-based nanocomposites and soft computing predictioncitations
- 2023Hypereutectic aluminum alloys and composites: A reviewcitations
- 2023Enhancing of ZA-27 alloy wear characteristics by addition of small amount of SiC nanoparticles and its optimisation applying Taguchi methodcitations
- 2023Influence of friction riveting parameters on the dissimilar joint formation and strengthcitations
- 2023Ceramic matrix composites with carbon nanophases: Development, structure, mechanical and tribological properties and electrical conductivitycitations
- 2023A review on tribological properties of microcomposites with ZA-27 alloy matrix
- 2023Influence of the metal-plating additive “Valena” on wear of the spheroidal graphite cast iron microalloyed by Sn
- 2023Metal-metal composites with Zn-Al alloy base and addition of Ti microparticles reinforced with ceramic nanoparticles
- 2023Tribology of metal matrix micro- and nanocomposites
- 2022Optimization of parameters that affect wear of A356/Al<sub>2</sub>O<sub>3</sub> nanocomposites using RSM, ANN, GA and PSO methodscitations
- 2022Influence of friction riveting parameters on the dissimilar joint formation and strengthcitations
- 2021Production, Microstructure and Tribological Properties of Zn-Al/Ti Metal-Metal Composites Reinforced with Alumina Nanoparticlescitations
- 2021Studies on structural, mechanical and erosive wear properties of ZA-27 alloy-based micro-nanocompositescitations
- 2020Influence of secondary phases in A356 MMCs on their mechanical properties at macro- and nanoscalecitations
- 2019Parametric optimization of the aluminium nanocomposites wear ratecitations
- 2019Microstructural and basic mechanical characteristics of ZA27 alloy-based nanocomposites synthesized by mechanical milling and compocastingcitations
- 2019Tribological characterisation in dry sliding conditions of compocasted hybrid A356/SiCp/Grp composites with graphite macroparticlescitations
- 2019Erosive wear properties of ZA-27 alloy-based nanocomposites: Influence of type, amount, and size of nanoparticle reinforcementscitations
- 2017A review on tribological properties of microcomposites with ZA-27 alloy matrix
- 2016Tribological properties of aluminium matrix nanocomposites
- 2016Optimization and prediction of aluminium composite wear using Taguchi design and artificial neural network
- 2013A review of the tribological properties of PTFE composites filled with glass, graphite, carbon or bronze reinforcement
Places of action
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article
Optimization and prediction of aluminium composite wear using Taguchi design and artificial neural network
Abstract
This paper analyses wear behaviour of Al-Si alloy A356 (AlSi7Mg) based composite reinforced with 10 wt. % SiC, and compare it with the base A356 alloy. Composite are obtained using the compocasting procedure. Tribological testing have been conducted on a block-on-disc tribometer with three varying loads (10, 20 and 30 N) and three sliding speeds (0.25, 0.5 and 1 m/s), under dry sliding conditions. Sliding distance of 300 m was constant. The goal of the paper was to optimize the influencing parameters in order to minimize specific wear rate using the Taguchi method. The analysis showed that the sliding speed has the greatest influence on specific wear rate (39.5 %), followed by the load (23.6 %), and the interaction between sliding speed and load (19.4 %). A regression analysis and experiment corroboration was conducted in order to verify the results of the optimization. Specific wear rate prediction was done using artificial neural network (ANN).