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Prediction of Friction Durability in Off-Road Applications Based on Mechanistic Understanding of the Effects of Fluids and Surfaces on Clutch Friction
ISSN: 0148-7191, e-ISSN: 2688-3627
Published December 19, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
After new transmission lubricants are developed there is an extensive validation program where friction durability testing is performed on multiple clutch materials. Each durability test can run for long terms and the entire validation program can take much longer terms. A well designed lubricant and friction material will deliver the necessary friction control for construction equipment to operate at optimum level. A mechanistic construct has been evaluated to calculate friction durability in clutch systems based on fluid and surface tribological properties. Fluid properties include both boundary frictional and rheological effects. Surface properties include elastic modulus, surface roughness, asperity density and asperity tip radius. Using this mechanistic construct friction durability has been predicted. In the past, researchers in the field have often associated lubricant induced glazing of the friction material surface as the cause of the loss of friction control in clutch systems. In the current study, results show that wear is also a dominant cause of friction loss. In short clutch friction tests the rate of change in surface properties and fluid properties have been determined. Fluid friction properties do not change in these tests and surface properties of materials can change significantly. Based on the rate of change in surface properties, friction durability predictions have been made in order to shorten product validation time.
CitationDevlin, M., Adhvaryu, A., Cameron, T., Kariwa, S. et al., "Prediction of Friction Durability in Off-Road Applications Based on Mechanistic Understanding of the Effects of Fluids and Surfaces on Clutch Friction," SAE Technical Paper 2019-01-2339, 2019, https://doi.org/10.4271/2019-01-2339.
Data Sets - Support Documents
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