Tyre wear particles: an abundant yet widely unreported microplastic?

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.

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Rubber - Generation and collection of tyre and road wear particles (TRWP) - Road simulator laboratory method

10.3403/30370887 ◽

2018 ◽

Keyword(s):

Laboratory Method ◽

Wear Particles

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TIRE-WEAR PARTICLES AS A MAJOR COMPONENT OF MICROPLASTICS IN THE ENVIRONMENT

10.1130/abs/2018am-319219 ◽

2018 ◽

Author(s):

Reto Gieré ◽

◽

Frank Sommer ◽

Volker Dietze ◽

Anja Baum ◽

...

Keyword(s):

Wear Particles ◽

Tire Wear

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Multi-Objective Optimisation of Tyre and Suspension Parameters during Cornering for Different Road Roughness Profiles

Applied Sciences ◽

10.3390/app11135934 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5934

Author(s):

Georgios Papaioannou ◽

Jenny Jerrelind ◽

Lars Drugge

Keyword(s):

Optimal Solution ◽

Design Parameters ◽

Road Vehicles ◽

Propulsion Systems ◽

Vehicle Handling ◽

Optimum Parameters ◽

Trade Offs ◽

Road Roughness ◽

Control Technologies ◽

Tyre Wear

Effective emission control technologies and novel propulsion systems have been developed for road vehicles, decreasing exhaust particle emissions. However, work has to be done on non-exhaust traffic related sources such as tyre–road interaction and tyre wear. Given that both are inevitable in road vehicles, efforts for assessing and minimising tyre wear should be considered. The amount of tyre wear is because of internal (tyre structure, manufacturing, etc.) and external (suspension configuration, speed, road surface, etc.) factors. In this work, the emphasis is on the optimisation of such parameters for minimising tyre wear, but also enhancing occupant’s comfort and improving vehicle handling. In addition to the search for the optimum parameters, the optimisation is also used as a tool to identify and highlight potential trade-offs between the objectives and the various design parameters. Hence, initially, the tyre design (based on some chosen tyre parameters) is optimised with regards to the above-mentioned objectives, for a vehicle while cornering over both Class A and B road roughness profiles. Afterwards, an optimal solution is sought between the Pareto alternatives provided by the two road cases, in order for the tyre wear levels to be less affected under different road profiles. Therefore, it is required that the tyre parameters are as close possible and that they provide similar tyre wear in both road cases. Then, the identified tyre design is adopted and the optimum suspension design is sought for the two road cases for both passive and semi-active suspension types. From the results, significant conclusions regarding how tyre wear behaves with regards to passenger comfort and vehicle handling are extracted, while the results illustrate where the optimum suspension and tyre parameters have converged trying to compromise among the above objectives under different road types and how suspension types, passive and semi-active, could compromise among all of them more optimally.

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Organic Markers of Tire and Road Wear Particles in Sediments and Soils: Transformation Products of Major Antiozonants as Promising Candidates

Environmental Science & Technology ◽

10.1021/acs.est.1c02723 ◽

2021 ◽

Author(s):

Philipp Klöckner ◽

Bettina Seiwert ◽

Stephan Wagner ◽

Thorsten Reemtsma

Keyword(s):

Transformation Products ◽

Wear Particles ◽

Organic Markers

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Morphological residual convolutional neural network (M-RCNN) for intelligent recognition of wear particles from artificial joints

Friction ◽

10.1007/s40544-021-0516-2 ◽

2021 ◽

Author(s):

Xiaobin Hu ◽

Jian Song ◽

Zhenhua Liao ◽

Yuhong Liu ◽

Jian Gao ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Data Augmentation ◽

Support Vector ◽

Wear Particles ◽

Artificial Joints ◽

Performance Deterioration ◽

Overall Performance ◽

Correct Category ◽

Intelligent Recognition

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.

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