scholarly journals Effect of wear particles and roughness on nanoscale friction

2022 ◽  
Vol 6 (1) ◽  
Author(s):  
Tobias Brink ◽  
Enrico Milanese ◽  
Jean-François Molinari
2019 ◽  
Vol 7 (SI-TeMIC18) ◽  
Author(s):  
Norhanifah Abdul Rahman ◽  
Matzaini Katon Katon ◽  
Nurina Alya Zulkifli Zulkifli

Automatic Transmission (AT) system is efficient in the aspects of vehicle safety, comfort, reliability and driving performance. The objectives of this paper are to collect the oil samples from AT systems of engine bus according to manufacturer's recommendations and analyse collected oil samples using oil analysis technique. The sample transmission fluid which was taken from the AT gearbox has been experimentally analyzed. The oil samples were taken with an interval of 5,000km, 30,000km, 50,000km, 80,000km, 180,000km and 300,000km for AT bus operation. These samples then have been analyzed by comparing between new and used transmission fluid using Fourier Transform Infrared (FTIR) spectroscopy. Oil analysis by FTIR is a form of Predictive Maintenance (PdM) to avoid major failure in machine elements. Most machine elements are not easily accessible in the transmission system. Having a reliable technique would avoid the needs to open the components unnecessarily, hence, help to prevent catastrophic failure which are very costly, and ease of regular monitoring. In order to identify the major failures of automatic gearbox, forecasts can be made regarding the lube transmission fluid analysis test. By using this test, the minor problems can be determined before they become major failures. At the end of this research, the wear particles profile for interval mileage of AT system was obtained. Keywords: Wear, Automatic Transmission (AT), Transmission fluid, Fourier Transform Infrared (FTIR), Oil analysis.


2018 ◽  
Author(s):  
Reto Gieré ◽  
◽  
Frank Sommer ◽  
Volker Dietze ◽  
Anja Baum ◽  
...  
Keyword(s):  

2020 ◽  
Vol 27 (15) ◽  
pp. 18345-18354 ◽  
Author(s):  
Lydia J. Knight ◽  
Florence N. F. Parker-Jurd ◽  
Maya Al-Sid-Cheikh ◽  
Richard C. Thompson
Keyword(s):  

Friction ◽  
2021 ◽  
Author(s):  
Xiaobin Hu ◽  
Jian Song ◽  
Zhenhua Liao ◽  
Yuhong Liu ◽  
Jian Gao ◽  
...  

AbstractFinding the correct category of wear particles is important to understand the tribological behavior. However, manual identification is tedious and time-consuming. We here propose an automatic morphological residual convolutional neural network (M-RCNN), exploiting the residual knowledge and morphological priors between various particle types. We also employ data augmentation to prevent performance deterioration caused by the extremely imbalanced problem of class distribution. Experimental results indicate that our morphological priors are distinguishable and beneficial to largely boosting overall performance. M-RCNN demonstrates a much higher accuracy (0.940) than the deep residual network (0.845) and support vector machine (0.821). This work provides an effective solution for automatically identifying wear particles and can be a powerful tool to further analyze the failure mechanisms of artificial joints.


2015 ◽  
Vol 642 ◽  
pp. 8-12
Author(s):  
William W.F. Chong ◽  
Miguel de La Cruz

The paper introduces an alternative approach to predict boundary friction for rough surfaces at micros-scale through the empirical integration of asperity-like nanoscale friction measurements. The nanoscale friction is measured using an atomic force microscope (AFM) tip sliding on a steel plate, confining the test lubricant, i.e. base oil for the fully formulated SAE grade 10w40. The approach, based on the Greenwood and Tripp’s friction model, is combined with the modified Elrod’s cavitation algorithm in order to predict the friction generated by a slider-bearing test rig. The numerical simulation results, using an improved boundary friction model, showed good agreement with the measured friction data.


2012 ◽  
Vol 23 (4) ◽  
pp. 891-901 ◽  
Author(s):  
Fang Lu ◽  
Matt Royle ◽  
Ferdinand V. Lali ◽  
Alister J. Hart ◽  
Simon Collins ◽  
...  

2006 ◽  
Vol 9 (9) ◽  
pp. 15
Author(s):  
Paula Gould
Keyword(s):  

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