Lamb Wave Flaw Classification in Al Plates Using Time Reversal and Deep Neural Networks
| Autor: | Ziqiao Tang, Sung-Jin Song, Yun-Taek Yeom, Nauman Munir, Taek-Gyu Lee |
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| Rok vydání: | 2019 |
| Předmět: |
010302 applied physics
Physics Signal processing Artificial neural network Noise (signal processing) Acoustics General Physics and Astronomy 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences Signal symbols.namesake Additive white Gaussian noise Transducer Lamb waves 0103 physical sciences symbols Ultrasonic sensor Physics::Atomic Physics 0210 nano-technology |
| Zdroj: | Journal of the Korean Physical Society. 75:978-984 |
| ISSN: | 1976-8524 0374-4884 |
| DOI: | 10.3938/jkps.75.978 |
| Popis: | The interpretation of Lamb wave signals in real transducers needs careful excitation of Lamb wave mode and complex signal processing technique. In this study, the low mode antisymmetric Lamb wave is generated at one side and recorded at the other side, in a solid plate with different length and thickness. The time reversal technique is applied to Lamb wave signal processing and deep neural network is used to classify various Lamb wave FE signals. In industrial applications, the ultrasonic signals are not noise free. The white Gaussian noise is added to the FE simulation signals to augment the database. Without extracting the features of ultrasonic signal, the applied deep neural network to the ultrasonic noisy signals shows a high accuracy performance to classify the defect one and non-defect ones. |
| Databáze: | OpenAIRE |
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