Inductive thermography is a non-destructive inspection technique. The sample is heated with a short heating pulse and an IR camera records the surface temperature, which is then evaluated to a phase image by Fourier transform. The technique can be well applied for detecting cracks in metals. Additionally, it has also the advantage of providing information about the depth of the crack. Larger contrast is an indication of deeper cracks, while small contrasts refer to shallow cracks. Therefore, the phase contrast can be used to make an estimation of the considered crack. In order to investigate these capabilities, short cracks (length =0.3-3mm) were created in Inconel 718 welded samples by a Varestraint test machine. The samples were then inspected with inductive thermography, computer tomography (CT) and by fluorescent penetrant test (FPT). The crack lengths obtained by all the three methods are compared. The dependency of the phase contrast on the crack depth and length is then analyzed in comparison to the CT results. Finally, additional finite element simulations were carried out and compared to the experimental results.
This work presents a convolutional neural network (CNN), trained on simulated data and used for the detection of cracks resulted by inductive thermography measurements. In inductive thermography the sample under study is heated with a short heating pulse and an infrared (IR) camera records the emitted surface radiation during both heating and cooling. The recorded IR sequence is then evaluated to a phase image using Fourier transform. In phase images, short surface cracks become visible due to the hot spots around the defect tips and due to the low phase value along the crack line. For the training of a deep neural network many images are necessary, which should be different from the images to be evaluated. This is why FEM simulations have been carried out varying crack length, depth and inclination angle. Additional Gaussian noise and augmentation have been added to these simulated images before using them to train a CNN. Samples with real cracks along a weld have been created in Inconel 718, and the CNN, trained on the simulation results, has been used for semantic segmentation of these real samples’ phase images, in order to identify the defects. Additionally, the samples were investigated by computer tomography, and this 3D information of the cracks is compared to the phase image results.
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