Machine Learning Composite-Nanoparticle-Enriched Lubricant Oil Development for Improved Frictional Performance—An Experiment
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 boundar...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Lubricants |
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| Online Access: | https://www.mdpi.com/2075-4442/11/6/254 |
| _version_ | 1797593768547844096 |
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| author | Ali Usman Saad Arif Ahmed Hassan Raja Reijo Kouhia Andreas Almqvist Marcus Liwicki |
| author_facet | Ali Usman Saad Arif Ahmed Hassan Raja Reijo Kouhia Andreas Almqvist Marcus Liwicki |
| author_sort | Ali Usman |
| collection | DOAJ |
| description | 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 (R<sup>2</sup>). The ANN showed the best prediction performance among all ML models, with 2.22 × 10<sup>−3</sup> RMSE, 4.92 × 10<sup>−6</sup> MSE, 2.1 × 10<sup>−3</sup> MAE, and 0.99 R<sup>2</sup>. 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. |
| first_indexed | 2024-03-11T02:13:11Z |
| format | Article |
| id | doaj.art-220c271524e64d459c4ec51a2c0de62b |
| institution | Directory Open Access Journal |
| issn | 2075-4442 |
| language | English |
| last_indexed | 2024-03-11T02:13:11Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Lubricants |
| spelling | doaj.art-220c271524e64d459c4ec51a2c0de62b2023-11-18T11:19:52ZengMDPI AGLubricants2075-44422023-06-0111625410.3390/lubricants11060254Machine Learning Composite-Nanoparticle-Enriched Lubricant Oil Development for Improved Frictional Performance—An ExperimentAli Usman0Saad Arif1Ahmed Hassan Raja2Reijo Kouhia3Andreas Almqvist4Marcus Liwicki5Structural Engineering, Faculty of Built Environment, Tampere University, 33720 Tampere, FinlandDepartment of Mechanical Engineering, HITEC University, Taxila 47080, PakistanDepartment of Mechanical Engineering, COMSATS University Islamabad, Wah Cantt. 47040, PakistanStructural Engineering, Faculty of Built Environment, Tampere University, 33720 Tampere, FinlandDepartment of Engineering Sciences and Mathematics, Division of Machine Elements, Luleå University of Technology, 97187 Luleå, SwedenDepartment of Computer Science, Electrical and Space Engineering, EISLab, Division of Machine Learning, Luleå University of Technology, 97187 Luleå, SwedenImproving 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 (R<sup>2</sup>). The ANN showed the best prediction performance among all ML models, with 2.22 × 10<sup>−3</sup> RMSE, 4.92 × 10<sup>−6</sup> MSE, 2.1 × 10<sup>−3</sup> MAE, and 0.99 R<sup>2</sup>. 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.https://www.mdpi.com/2075-4442/11/6/254machine learningfrictionlubricationnanoparticlestribologyartificial neural network |
| spellingShingle | Ali Usman Saad Arif Ahmed Hassan Raja Reijo Kouhia Andreas Almqvist Marcus Liwicki Machine Learning Composite-Nanoparticle-Enriched Lubricant Oil Development for Improved Frictional Performance—An Experiment Lubricants machine learning friction lubrication nanoparticles tribology artificial neural network |
| title | Machine Learning Composite-Nanoparticle-Enriched Lubricant Oil Development for Improved Frictional Performance—An Experiment |
| title_full | Machine Learning Composite-Nanoparticle-Enriched Lubricant Oil Development for Improved Frictional Performance—An Experiment |
| title_fullStr | Machine Learning Composite-Nanoparticle-Enriched Lubricant Oil Development for Improved Frictional Performance—An Experiment |
| title_full_unstemmed | Machine Learning Composite-Nanoparticle-Enriched Lubricant Oil Development for Improved Frictional Performance—An Experiment |
| title_short | Machine Learning Composite-Nanoparticle-Enriched Lubricant Oil Development for Improved Frictional Performance—An Experiment |
| title_sort | machine learning composite nanoparticle enriched lubricant oil development for improved frictional performance an experiment |
| topic | machine learning friction lubrication nanoparticles tribology artificial neural network |
| url | https://www.mdpi.com/2075-4442/11/6/254 |
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