| 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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Kouhia, Reijo
Tampere University of Technology
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (10/10 displayed)
- 2024Bonding of ceramics to silver-coated titanium—A combined theoretical and experimental study
- 2023Numerical Modelling of Thermal Weakening Effect on Compressive Strength of Concrete
- 2023Machine Learning Composite-Nanoparticle-Enriched Lubricant Oil Development for Improved Frictional Performance—An Experimentcitations
- 2022Strength of Ice in Brittle Regime—Multiscale Modelling Approachcitations
- 2022Modelling the effect of concrete cement composition on its strength and failure behaviorcitations
- 2019Implementation of a continuum damage model for creep fracture and fatigue analyses to ANSYS
- 2017On the Modelling of Creep Fracture and Fatigue
- 2017Metallien virumismurron ja virumisväsymisen mallintaminen
- 2016A continuum damage model for creep fracture and fatigue analysescitations
- 2016Modeling and experimental verification of magneto‐mechanical energy harvesting device based on construction steel
Places of action
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article
Machine Learning Composite-Nanoparticle-Enriched Lubricant Oil Development for Improved Frictional Performance—An Experiment
Abstract
Improving the frictional response of a functional surface interface has been a significant research concern. During the last couple of decades, lubricant oils have been enriched with several additives to obtain formulations that can meet the requirements of different lubricating regimes from boundary to full-film hydrodynamic lubrication. The possibility to improve the tribological performance of lubricating oils using various types of nanoparticles has been investigated. In this study, we proposed a data-driven approach that utilizes machine learning (ML) techniques to optimize the composition of a hybrid oil by adding ceramic and carbon-based nanoparticles in varying concentrations to the base oil. Supervised-learning-based regression methods including support vector machines, random forest trees, and artificial neural network (ANN) models are developed to capture the inherent non-linear behavior of the nano lubricants. The ANN hyperparameters were fine-tuned with Bayesian optimization. The regression performance is evaluated with multiple assessment metrics such as the root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R2). The ANN showed the best prediction performance among all ML models, with 2.22 × 10−3 RMSE, 4.92 × 10−6 MSE, 2.1 × 10−3 MAE, and 0.99 R2. The computational models’ performance curves for the different nanoparticles and how the composition affects the interface were investigated. The results show that the composition of the optimized hybrid oil was highly dependent on the lubrication regime and that the coefficient of friction was significantly reduced when optimal concentrations of ceramic and carbon-based nanoparticles are added to the base oil. The proposed research work has potential applications in designing hybrid nano lubricants to achieve optimized tribological performance in changing lubrication regimes. ; Validerad;2023;Nivå 2;2023-06-12 (sofila);