Ultrasonic Thickness Estimation using Multimodal Guided Lamb Waves Generated by EMAT
Conference: Publication Date: 1 April 2019Testing Method:
The objective of this paper is to study how the selection of the coil and the frequency affects the received modes in guided Lamb waves, with the objective of analyzing the best configuration for determining the depth of a given defect in a metallic pipe with the minimum error. Studies of the size of the damages with all the extracted parameters are then used to propose estimators of the residual thickness, considering amplitude and phase information in one or several modes. Results demonstrate the suitability of the proposal, improving the estimation of the residual thickness when two simultaneous modes are used, as well as the range of possibilities that the coil and frequency selection offers.
- Green, R.E., 2004. “Non-contact ultrasonic techniques”. Ultrasonics, 42, 9–16.
- Zhai, G., Jiang, T., Kang, L., 2014. “Analysis of multiple wavelengths of Lamb waves generated by meanderline
coil EMATs”. Ultrasonics, 54, 632–636.
- Salzburger, H.J., Niese, F., Dobmann, G., 2012. “EMAT pipe inspection with guided waves”. Welding in the
world, 56, 35–43.
- Demma, A., 2003. The interaction of guided waves with discontinuities in structures. PhD thesis, University of
- Cobb, A.C., Fisher, J.L., 2016. “Flaw depth sizing using guided waves”. AIP Conference Proceedings. AIP
Publishing, Vol. 1706, p. 030013.
- García-Gómez, J., Bautista-Durán, M., Gil-Pita, R., Romero-Camacho, A., Jimenez-Garrido, J.A., Garcia-
Benavides,V., 2018, “Smart Sound Processing for Residual Thickness Estimation using Guided Lamb Waves
generated by EMAT”. 27th ASNT Research Symposium, 99-105.
- Weisz, L., 2016. “Pattern Recognition Statistical Structural And Neural Approaches”. Pattern Recognition, 1,
- Hagan, M.T.; Menhaj, M.B., 1994. “Training feedforward networks with the Marquardt algorithm”. IEEE
transactions on Neural Networks, 5, 989–993.
15 Page Views
0 PDF Downloads
0 Facebook Shares