Reconstruction and Contact Mechanical Evaluation of Railway Rail Surface Topography from Imaging

Sammanfattning

Rail surface roughness plays a critical role in wheel–rail contact mechanics, influencing friction, wear, rolling contact fatigue, vibration, and noise generation. Accurate characterisation of rail surface topography is therefore important for evaluating grinding quality and assessing rail condition. Conventional high-resolution measurement techniques, such as optical interferometry, provide detailed surface information but are expensive, time-consuming, and generally limited to laboratory environments. Field equipment, such as profilometry, has a limited field of view and therefore leaves most of the rail unmeasured. This thesis investigates the feasibility of reconstructing three-dimensional rail surface topography from conventional two-dimensional image data as a potential low-cost alternative for surface characterisation. A controlled imaging system was developed to acquire repeatable grayscale images of ground rail surfaces under predefined illumination conditions. High-resolution surface topography measurements were obtained using a Zygo NewView 9000 optical interferometer and were used as ground truth throughout the study. The image and topography datasets were spatially aligned and processed to create paired training data for machine learning-based surface reconstruction. A convolutional neural network based on the U-Net architecture was implemented to estimate surface height fields from the grayscale images. The network was trained using patches extracted from photographs acquired with both an industrial camera, Allied Vision Mako U-503B, and a smartphone camera, iPhone 17 Pro, under multiple illumination configurations matched with their corresponding interferometric topography measurements. Reconstruction performance was evaluated using statistical roughness descriptors, point-wise error metrics, and contact mechanical analysis using the boundary element method for contact mechanical simulations. The results demonstrate that it is possible to reconstruct the surface topography from conventional image data when imaging conditions are carefully controlled. The developed methodology shows the potential of image-based rail surface reconstruction and suggests that future field applications are feasible. The relationship between image intensity and surface height was found to be highly complex due to the reflective and anisotropic nature of machined rail surfaces, which limits reconstruction accuracy and highlights the need for further research and development in the area before the method can be considered for field applications. Overall, the study establishes a complete framework for image-based rail surface reconstruction and validation, combining controlled imaging, machine learning, interferometric measurements, and contact mechanical evaluation. While the current approach is not yet capable of replacing established metrology techniques, the results demonstrate the feasibility of the concept and provide a foundation for future development of cost-effective and scalable rail surface assessment methods.