Numerical and experimental investigations on mode conversion of guided waves in partially immersed plates

Hu, Xianwen; Ng, Ching‐Tai; Kotousov, Andrei

doi:10.1016/j.measurement.2022.110750

Cited by 9 publications

(1 citation statement)

References 49 publications

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“…This variation of the nature can noise the post processing and induce error detection. So many authors have studied the conversion phenomenon like [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Towards an optimization of automatic defect detection by artificial neural network using Lamb waves

Salah,

Barra Ndiaye

2024

IJ-AI

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This paper presents a damage detection method based on the inverse pattern recognition technique by artificial neural network (ANN) using ultrasonic waves. Lamb waves are guided elastic waves, are widely employed in nondestructive testing thanks to their attractive properties such as their sensitivity to the small defects. In this work, finite element method was conducted by Abaqus to study Lamb modes propagation. A data collection is performed by the signals recorded from the sensor of 300 models: healthy and damaged plates excited by a tone burst signal with the frequencies: 100 kHz, 125 kHz, 150 kHz, 175 kHz, 200 kHz, and 225 kHz. The captured signals in undamaged plat are the baseline, whereas the signals measured in damaged plates are recorded for various positions of external rectangular defects. To reduce the amount of training data, only two peaks of measured signals are required to be the input of the model. Continuous wavelet transform (CWT) was adopted to calculate the key features of the signal in the time domain. The feed forward neural network is implemented using MATLAB program. The data are divided as follows: 70% for training the model, 25% for the validation, and 5% for the test. The proposed model is accurate estimating the position of the defect with an accuracy of 99.98%.

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“…This variation of the nature can noise the post processing and induce error detection. So many authors have studied the conversion phenomenon like [5], [6].…”

Section: Introductionmentioning

confidence: 99%