Synchrotron and neural network analysis of the influence of composition and heat treatment on the rolling contact fatigue of hypereutectoid pearlitic steels
Materials Science & Engineering A: Structural Materials: Properties, Microstructure and Processing
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Solano Alvarez, W., Peet, M., Pickering, E., Jaiswal, J., Bevan, A., & Bhadeshia, H. (2017). Synchrotron and neural network analysis of the influence of composition and heat treatment on the rolling contact fatigue of hypereutectoid pearlitic steels. Materials Science & Engineering A: Structural Materials: Properties, Microstructure and Processing, 707 259-269. https://doi.org/10.1016/j.msea.2017.09.045
A series of experimental hypereutectoid pearlitic steels were tested under rolling contact sliding conditions using a lubricated twin-disc setup to study the influence of different chemical compositions and heat treatments on rolling contact fatigue life. Tested samples were then characterised using microscopy and synchrotron measurements as a function of depth from the contact surface. Results, analysed through neural networks, indicate that the most influential factor in lengthening the number of cycles to crack initiation of hypereutectoid steels is hardness, attained by increasing the cooling rate from the hot rolling temperature, but adequate alloying additions can enhance it further. The harder, fast-cooled samples displayed less plastic flow at the surface than the softer slow-cooled ones. With regard to chemical composition, silicon was found to strengthen the ferrite thus reducing strain incompatibilities with the cementite, preventing in this way the fragmentation and eventual dissolution of the lamellae. This is beneficial since larger depths of cementite dissolution were found in samples with lower cycles to crack initiation for a given cooling rate (hardness). Samples containing vanadium lasted longer and displayed less plastic deformation at the surface than those without, at a similar hardness.
synchrotron, cementite dissolution, hypereutectoid rail steels, rolling contact fatigue, neural network, pearlite
The authors are thankful to Dr Andreas Stark from the Institute of Materials Research of the Helmholtz-Zentrum Geesthacht for his help with synchrotron measurements, to Dr Giorgio Divitini of the Electron Microscopy Group in the Department of Materials Science and Metallurgy for his help with TEM/EDS, and to the Phase Transformations Group members Dr Neelabhro Bhattacharya, Ailsa Kiely, and Dr Arunim Ray for their help with synchrotron data conversion and analysis. This research was financed under EPSRC grant EP/M023303/1 “Designing steel composition and microstructure to better resist degradation during wheel-rail contact” in collaboration with the Rail Safety and Standards Board (RSSB), the Department of Transport, the University of Leeds, and Cranfield University Work by M. J. Peet was supported by the Medical Research Council Grant No. U105192715.
External DOI: https://doi.org/10.1016/j.msea.2017.09.045
This record's URL: https://www.repository.cam.ac.uk/handle/1810/274790
Attribution 4.0 International
Licence URL: http://creativecommons.org/licenses/by/4.0/
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