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.
The quality control of structures and fuselages in both the wind-turbine and solar sectors is a fundamental part that allows a lifetime assessment of their elements, from its initial assembly to the recurring inspection cycles. Automating the active thermography on this scale, cannot be achieved with conventional industrial robots. Unmanned vehicles, such UAVs and UGVs, present distinctive advantages that should certainly be exploited, but, its inherent static motion is one of the main stumbling blocks towards its use in an active thermography inspection. In this paper, a two-step digital stabilization scheme has demonstrated its efficacy in real defects located in both a wind blade and solar panel. The combination of a featurebased registration algorithm and a dense parametric optical flow direct alignment has enabled the pseudo-static reconstruction of the thermograms. The adopted experimental methodology, employing a robot with both halogens and IR camera, subjected to random motions with varying speed and amplitudes, has allowed a direct repeatable comparison of static and stabilized phase images. The phase image contrast comparison of both static and dynamic tests, have been carried out on a flat bottom hole (FBH) wind blade GFRP sample, showing nearly identical phase contrast with marginal differences. Likewise, a real GFRP wind-blade impact delamination defect has also reached a close phase contrast regarding its counterpart, albeit with a decreased contrast. Additionally, the registration algorithm has been used to stitch the individual frames, derived from a dynamic recording of an electroluminescent solar panel, to allow for a unified detection and mapping of defects.
In this work Induction Thermography has been applied to inspect Inconel 718 EBW and TIGWelded components, focusing on the optimisation of both the induction tests and the algorithms needed for an automatic defect detection. The aim is 1) to ensure the inspectability of the component regardless of its geometry and 2) develop a robust automatic defect detection without false positives. For the first part, experiments have been carried out considering different inductor configurations (different windings, ferrite sections and geometries) and relative orientations between the inductor and the sample to be inspected to determine the importance of each magnitude. In the second part the work several thermal processing techniques have been tested: Fast Fourier Transform (FFT), Singular Value Decomposition (SVD) and Higher Order Statistical (HOS) analysis, to achieve images of higher quality (less noisy). This will improve the results of the previously developed detection algorithm (pDFT), diminishing the existing false positives. The second part of the work deals with the improvement of the automatic defect detection, based on the previously developed pDFT algorithm, which already provides an effective method of determining crack location, length and orientation. Hence, in this work the focus has been put on improving the processing in order to provide to the algorithm thermal processed images of higher quality (less noisy). In this way, the probability of detection failure will be diminished. Several processing algorithms have been tested: Fast Fourier Transform (FFT) and the Scaled Peak Amplitude, Singular Value Decomposition (SVD) and Higher Order Statistical (HOS) analysis. Then, to determine which is the best of them, a Signal to Noise Ratio (SNR) filter has been applied in the defective areas, looking always for the highest values.
Lock-in vibrothermography is used to characterize vertical kissing and open cracks in metals. In this technique the crack heats up during ultrasound excitation due mainly to friction between the defect’s faces. We have solved the inverse problem, consisting in determining the heat source distribution produced at cracks under amplitude modulated ultrasound excitation, which is an ill-posed inverse problem. As a consequence the minimization of the residual is unstable. We have stabilized the algorithm introducing a penalty term based on Total Variation functional. In the inversion, we combine amplitude and phase surface temperature data obtained at several modulation frequencies. Inversions of synthetic data with added noise indicate that compact heat sources are characterized accurately and that the particular upper contours can be retrieved for shallow heat sources. The overall shape of open and homogeneous semicircular strip-shaped heat sources representing open half-penny cracks can also be retrieved but the reconstruction of the deeper end of the heat source loses contrast. Angle-, radius- and depth-dependent inhomogeneous heat flux distributions within these semicircular strips can also be qualitatively characterized. Reconstructions of experimental data taken on samples containing calibrated heat sources confirm the predictions from reconstructions of synthetic data. We also present inversions of experimental data obtained from a real welded Inconel 718 specimen. The results are in good qualitative agreement with the results of liquids penetrants testing.
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