Effect of plastic deformation and fretting wear on the fretting fatigue of scaled railway axles
Source abstract
Abstract
Abstract To investigate the effect of plastic deformation and fretting wear on the fretting fatigue of scaled railway axles, three fatigue assessment methodologies are developed, namely elastic-fatigue, elastoplastic-fatigue and wear-fatigue methodologies. Elastic material behaviour, elasto-plastic material behaviour and fretting wear are taken into consideration in these three methodologies, respectively. A comparison of the simulation and experimental results indicates that the profile evolution of the fretting scar is mainly caused by fretting wear. The wear-fatigue methodology provides a better prediction of the fretting scar width and crack initiation site than the elastic-fatigue and elastoplastic-fatigue methodologies. With respect to the crack initiation life prediction, a relatively accurate prediction is obtained on using the elastoplastic-fatigue and wear-fatigue methodologies in the short-life region, while the initiation life predicted using the elastoplastic-fatigue methodology is shorter than that predicted by the wear-fatigue methodology in the long-life region. The influence of the diameter ratio on the fretting fatigue of scaled railway axles is then studied. The obtained simulation results show that an increment in the diameter ratio can reduce the fretting wear and relative slip range by alleviating the stress concentration, which thereby improves the fatigue strength of scaled railway axles. There exists a critical value of diameter ratio between 1.092 and 1.116, below which the fretting scar width and relative slip range are highly influenced by variations in the diameter ratio. This is in agreement with published experimental results.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Mechanical stress and fatigue analysis · Vibration and Dynamic Analysis · Engineering Structural Analysis Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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