Dr. Rafael Hidalgo-Gato Profile

Dr. Rafael Hidalgo-Gato

at Lortek S.Coop.

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Proceedings Article | 18 May 2020 Presentation + Paper

Induction thermography-based inspection of EBW and TIG welded Inconel 718 components: steps towards industrialization

Eider Gorostegui-Colinas, Rafael Hidalgo-Gato, Pablo López de Uralde, Beñat Urtasun Marco, Ander Muniategui Merino

Proceedings Volume 11409, 114090D (2020) https://doi.org/10.1117/12.2558265

KEYWORDS: Inspection, Detection and tracking algorithms, Signal to noise ratio, Thermography, Defect detection, Image processing, Algorithm development, Statistical analysis, Fourier transforms, Manufacturing

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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.

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