Tribological behaviors of MoAlB ceramic in artificial seawater

Yu, Zhenxin; Wang, Shuai; Cheng, Jun; Chen, Jiao; Chen, Wenyuan; Sun, Qichun; Yang, Jun

doi:10.1016/j.triboint.2021.107345

Cited by 4 publications

(1 citation statement)

References 44 publications

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“…Wang et al [17] found that thicker copper coated with molybdenum disulfide had a lower friction coefficient but exhibited more severe wear. Yu et al [18] reported that hydrated silica tribofilm reduced the CoF of MoAlB ceramic to 0.12. Pham et al [19] showed that SiO 2 enhances the anti-oxidation of lubricants.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Composite-Nanoparticle-Enriched Lubricant Oil Development for Improved Frictional Performance—An Experiment

et al. 2023

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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.

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Section: Introductionmentioning

confidence: 99%