Abstract
The reliable prediction of wheel wear can help to reduce maintenance costs. With the help of two common approaches (statistical, contact mechanics based), it is possible to predict wheel profile shapes either quickly and precisely, but for a unique operating situation only, or for varying operating scenarios in a more time-consuming, but often less accurate way because so many, sometimes even unknown, input data are needed. There is no method available for predicting worn wheel profile shapes quickly, accurately, and generally. The hybrid approach presented in this work combines the two state of the art approaches mentioned above in order to exploit their advantages and eliminate their disadvantages. The new method was calibrated and validated on wheel measurement data taken from the field. A good agreement between measurements and predictions was observed when using maximum wheel-rail contact shear stresses as the wear measure in the methodology.
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Acknowledgments
The publication was written at Virtual Vehicle Research GmbH in Graz, Austria, together with all listed co-authors. The authors would like to acknowledge the financial support within the COMET K2 Competence Centers for Excellent Technologies from the Austrian Federal Ministry for Climate Action (BMK), the Austrian Federal Ministry for Digital and Economic Affairs (BMDW), the Province of Styria (Dept. 12) and the Styrian Business Promotion Agency (SFG). The Austrian Research Promotion Agency (FFG) has been authorised for the programme management. They would furthermore like to express their thanks to their supporting industrial and scientific project partners Siemens Mobility GmbH, voestalpine Rail Technology GmbH and the University of Sheffield.
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Hartwich, D. et al. (2022). A Fast, Reliable and Practical Method to Predict Wheel Profile Evolution. In: Orlova, A., Cole, D. (eds) Advances in Dynamics of Vehicles on Roads and Tracks II. IAVSD 2021. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-07305-2_55
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DOI: https://doi.org/10.1007/978-3-031-07305-2_55
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