| Title |
Enhancement and comparison of tomographic reconstruction images in plate-like structures of aircrafts for SHM application using guided waves |
| Authors |
Asokkumar, Aadhik |
| Full Text |
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| Is Part of |
e-Journal of nondestructive testing (eJNDT): proceedings of the 13th European Conference on Non-Destructive Testing (ECNDT 2023), 3-7 July 2023, Lisbon, Portugal.. Mayen : NDT.net. 2023, vol. 28, spec. iss. 8, p. 1-22.. ISSN 1435-4934 |
| Keywords [eng] |
SHM ; guided waves ; NDT ; ultrasonic testing ; damage imaging |
| Abstract [eng] |
Tomographic reconstruction using ultrasonic guided wave is a mathematical procedure to image the defect in the area under inspection. Currently, there are different methods of tomographic reconstruction algorithm such as Filtered Back Projection (FBP) algorithm, Reconstruction Algorithm for the Probabilistic Inspection of Damage (RAPID), iterative reconstruction algorithm etc. Typically for the tomographic reconstruction features such as the amplitude, velocity and attenuation of the signal are being used. But using some of the advanced signal processing method such as filtering, deconvolution, empirical mode decomposition, etc., it is possible to condition the signal before extracting the useful features for the tomographic reconstruction. This way, the resulting tomographic reconstruction image can be enhanced which can reveal added details and information about the defect when compared to the tomographic reconstruction performed the traditional way. In this work, such way to enhance the tomographic reconstruction image is demonstrated with data from an Aluminium-2024 metallic plate with notch type defect, one pultruded type Glass-Fibre Reinforced Plastic (GFRP) composite plate with impact defect and one GFRP composite plate with artificial defect. Tomographic reconstruction results of FBP and RAPID before and after enhancement are compared using the -3dB defect sizing method for the defect. Then parameters of the defects are calculated. |
| Published |
Mayen : NDT.net |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2023 |
| CC license |
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