Tribological performance study and prediction of copper coated by MoS2 based on GBRT method

Wang, Guoqing; Ruan, Yuling; Wang, Hongxing; Zhao, Gai; Cao, Xinxin; Li, Xingming; Ding, Qingjun

doi:10.1016/j.triboint.2022.108149

Cited by 18 publications

(5 citation statements)

References 30 publications

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“…The values of this study are similar to the results of other works. 46,47) MD simulation and experimental methods have many different conditions, such as different model scales, initial condition parameter settings, simulation environment, and other factors. Therefore, the simulation results will be different from the experiments.…”

Section: Effect Of Indenter Shapementioning

confidence: 99%

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Surface and interface mechanical properties of inorganic nanolayers

Huang,

Lai,

Fang

2024

Jpn. J. Appl. Phys.

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This study investigates the effects of the indenter on nickel coated by MoS2 during nanoindentation and nanoscratch through molecular dynamics (MD) simulations. The effects of indenter size, indenter shape, and scratch angle on the deformation and wear mechanisms of the material were studied. The results show that the size and shape of the indenter have significant effects under nanoindentation and scratching. A larger indenter size can lead to a larger deformation zone and more dislocations inside the workpiece, and the friction coefficient also decreases as the radius of the indenter increases. The friction coefficient at different scratch angles is not significant difference, which indicates that in the case of the monolayer of MoS2 coating has weakly dependent on the lattice orientation. In addition, different indenter shapes will cause differences in stress concentration and penetration depth inside the workpiece. The trapezoidal indenter also shows a higher friction coefficient and wear rate.

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Section: Effect Of Indenter Shapementioning

confidence: 99%

“…The results of previous experimental studies also indicated that the material combined with MoS 2 coating can improve wear resistance and reduce friction. 15,41,45,46)…”

Section: Effect Of Indenter Shapementioning

confidence: 99%

Surface and interface mechanical properties of inorganic nanolayers

Huang,

Lai,

Fang

2024

Jpn. J. Appl. Phys.

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“…The classic AdaBoost algorithm can only handle two-class learning tasks using exponential loss functions, while the gradient boosting method can handle various learning tasks (multiclassification, regression, ranking, etc.) by setting different differentiable loss functions, which greatly expands the scope of application of the algorithm [27]. The gradient boosting algorithm uses the negative gradient of the loss function as the residual fitting method.…”

Section: Gradient Boosted Regression Tree 231 Algorithm Overviewmentioning

confidence: 99%

Prediction of Daily Water Consumption in Residential Areas Based on Meteorologic Conditions—Applying Gradient Boosting Regression Tree Algorithm

Li,

Peng,

Zheng

et al. 2023

Water

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A more accurate way of water consumption forecasting can be used to help people develop a scheduling plan of water workers more targeting; therefore, this paper aims to establish a forecast model of daily water consumption based on meteorological conditions. At present, most studies of daily water consumption forecasts focus on historical data or single water use influencing factors; moreover, daily water consumption could be influenced by meteorologic conditions. The influence of complex meteorology factors on water consumption is analyzed based on a gradient-boosted regression tree (GBRT) model. The correlation of 10 meteorologic factors has been discussed and divided into 5 categories, including temperature factor, pressure factor, precipitation factor, sunshine factor, and wind factor. Through the GBRT algorithm, the daily water consumption of residential area could be predicted with a maximum error of ±8%. The results show that the average ground temperature (the feature importance accounts for 81% of the total) has the greatest impact on the daily water consumption of the residential community, followed by the somatosensory temperature (the feature importance accounts for 7% of the total). The method can provide the daily water consumption of water consumption nodes with higher precision for municipal water supply network model accuracy. It also provides a reference for water utility operation schemes and urban development planning.

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“…Zhao et al successfully predicted the friction coefficient and wear rate of a coating through an ML algorithm of a gradient boosting regression tree. The predictive accuracy for the friction coefficient and wear rate reached 94.6% and 96.3%, respectively [20]. Guo and his team introduced a signal-processing method based on friction noise to predict the tribological properties of polymers over a wide temperature range.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Tribological Properties of Polytetrafluoroethylene Composites Based on Experiments and Machine Learning

Yan,

Du,

Ren

et al. 2024

Polymers

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Because of the complex nonlinear relationship between working conditions, the prediction of tribological properties has become a difficult problem in the field of tribology. In this study, we employed three distinct machine learning (ML) models, namely random forest regression (RFR), gradient boosting regression (GBR), and extreme gradient boosting (XGBoost), to predict the tribological properties of polytetrafluoroethylene (PTFE) composites under high-speed and high-temperature conditions. Firstly, PTFE composites were successfully prepared, and tribological properties under different temperature, speed, and load conditions were studied in order to explore wear mechanisms. Then, the investigation focused on establishing correlations between the friction and wear of PTFE composites by testing these parameters through the prediction of the friction coefficient and wear rate. Importantly, the correlation results illustrated that the friction coefficient and wear rate gradually decreased with the increase in speed, which was also proven by the correlation coefficient. In addition, the GBR model could effectively predict the tribological properties of the PTFE composites. Furthermore, an analysis of relative importance revealed that both load and speed exerted a greater influence on the prediction of the friction coefficient and wear rate.

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Tribological performance study and prediction of copper coated by MoS2 based on GBRT method

Cited by 18 publications

References 30 publications

Surface and interface mechanical properties of inorganic nanolayers

Surface and interface mechanical properties of inorganic nanolayers

Prediction of Daily Water Consumption in Residential Areas Based on Meteorologic Conditions—Applying Gradient Boosting Regression Tree Algorithm

Prediction of the Tribological Properties of Polytetrafluoroethylene Composites Based on Experiments and Machine Learning

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