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    A Machine Learning Approach for Real-Time Wheel-Rail Interface Friction Estimation 

    Source: Journal of Tribology:;2023:;volume( 145 ):;issue: 009:;page 91102-1
    Author(s): Folorunso, Morinoye O.; Watson, Michael; Martin, Alan; Whittle, Jacob W.; Sutherland, Graham; Lewis, Roger
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Predicting friction at the wheel-rail interface is a key problem in the rail industry. Current forecasts give regional-level predictions, however, it is well known that friction conditions can change dramatically over a ...
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    A Discrete Element Model of High-Pressure Torsion Test to Assess the Effect of Particle Characteristics in the Interface 

    Source: Journal of Tribology:;2024:;volume( 146 ):;issue: 008:;page 81501-1
    Author(s): Zhang, Chao; Nadimi, Sadegh; Maramizonouz, Sadaf; Milledge, David; Lewis, Roger
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Sand particles have been used since the early stages of the railway industry to increase adhesion at the wheel–rail contact. However, there is a limited understanding of how sand particle characteristics affect the ...
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    Real-Time Measurement of Dynamic Wheel-Rail Contacts Using Ultrasonic Reflectometry 

    Source: Journal of Tribology:;2019:;volume( 141 ):;issue: 006:;page 61401
    Author(s): Zhou, Lu; Brunskill, Henry; Pletz, Martin; Daves, Werner; Scheriau, Stephan; Lewis, Roger
    Publisher: American Society of Mechanical Engineers (ASME)
    Abstract: The contact condition between the wheel and the rail is paramount to the lifespan, safety, and smooth operation of any rail network. The wheel/rail contact condition has been estimated, calculated, and simulated successfully ...
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