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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 104
PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE
Edited by: J. Pombo
Paper 104

Automated Measurement of Near-surface Plastic Shear Strain

G. Trummer1, K. Six1, C. Marte1, A. Meierhofer1 and C. Sommitsch2

1Virtual Vehicle Research Center, Graz, Austria
2Institute for Materials Science and Welding, Graz University of Technology, Austria

Full Bibliographic Reference for this paper
G. Trummer, K. Six, C. Marte, A. Meierhofer, C. Sommitsch, "Automated Measurement of Near-surface Plastic Shear Strain", in J. Pombo, (Editor), "Proceedings of the Second International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Stirlingshire, UK, Paper 104, 2014. doi:10.4203/ccp.104.104
Keywords: rolling contact, twin disc tests, pearlitic steel, plasticity, plastic shear strain, image analysis.

Summary
Severe plastic shear deformation and high shear strain gradients are frequently observed in the near-surface layer of tractive rolling contacts, such as in the contact between wheels and rails in railway systems. The variation of plastic shear strain with depth below the surface is important with respect to the interplay between rolling contact fatigue crack initiation and wear. To determine the distribution of plastic shear strain in a reliable and reproducible way from grayscale images of metallographic sections, an automated measurement method has been developed. This method uses local orientation information and local coherency information (a measure of structural alignment) to estimate the mean shear strain as a function of depth. No special specimen preparation is necessary prior to the measurement, such as the insertion of artificial markers or the manufacturing of gratings. The proposed method is validated by analyzing the orientation of inclusions, which have been found in parts of specimens. These inclusions serve as natural markers for the deformation process. The shear strain data from the inclusion analysis are in excellent agreement with the mean shear strain results obtained with the proposed automated method. The proposed method significantly reduces the measurement uncertainty of orientation data, compared to manual local orientation measurements. This is achieved by averaging orientation data over image areas. As an example of use, the plastic shear strain distributions in the near-surface layer of Twin Disc test specimens made of rail steel R260 and wheel steel R8 are analyzed with the proposed method.

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