Due to beneficial mechanical properties, cast manganese (Mn13) steel is used for premium grade railway turnout frogs worldwide. However, its coarse-grain structure makes common non-destructive testing (NDT) methods for defect detection used in this industry very difficult to apply. Inductive thermography is a NDT method well suited for this problem. Scanning inductive thermography is used to localise surface defects on the running surfaces of turnout frogs. Once localised, we propose additional static measurements to characterise the detected surface defects with respect to crack length, depth and penetration angle. Simulations with ANSYS Multiphysics are conducted to study the influence of different crack geometries as well as the influence of different excitation parameters. Validation measurements on samples with defined crack geometries are conducted. The results of both, simulation and measurements on samples, are used to characterize surface defects on actual manganese turnout frogs.
Scanning inductive thermography is a non-destructive inspection technique, which is suitable for detecting surface defects in long metallic work pieces. The work piece is moved below the inductor and the infrared (IR) camera, which is recording the surface temperature during the motion. To evaluate such measurements via phase image the recorded infrared image sequence must be reorganized according to the scanning speed. If the speed is not constant during the motion (e.g., due to manual scanning), visual fiducials (AprilTags) can be used in the camera’s field of view to register shifts between consecutive images. The main contribution of this work is image fusion, applied to scanning inductive thermography, combining the results of an infrared camera and a visual camera. An uncooled IR µ-bolometer camera with a thermal time constant of 8 ms is used for the infrared spectrum. Information of the motion speed during the scanning is acquired by the image registration, and it is used to deblur the IR image sequence before the evaluation to phase image via Fourier transform is performed. A second camera records the scanning process in the visual range. Using the AprilTags for registration, a panoramic view of the specimen is created. The results from both cameras are superimposed to improve the interpretation and localization of defects.
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