Deep Machine Learning for Acoustic Inspection of Metallic Medium
Acoustic non-destructive testing is widely used to detect signs of damage. However, an experienced technician is typically responsible for interpreting the result, and often the evaluation varies depending on the technician’s opinion. The evaluation is especially challenging when the acoustic signal...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Vibration |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-631X/5/3/30 |
| _version_ | 1797481482811342848 |
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| author | Brittney Jarreau Sanichiro Yoshida Emily Laprime |
| author_facet | Brittney Jarreau Sanichiro Yoshida Emily Laprime |
| author_sort | Brittney Jarreau |
| collection | DOAJ |
| description | Acoustic non-destructive testing is widely used to detect signs of damage. However, an experienced technician is typically responsible for interpreting the result, and often the evaluation varies depending on the technician’s opinion. The evaluation is especially challenging when the acoustic signal is analyzed in the near field as Fresnel range diffraction complicates the data. In this study, we propose a Convolutional Neural Network (CNN) algorithm to detect anomalies bearing in mind its future application to micro-scale specimens such as biomedical materials. Data are generated by emitting a continuous sound wave at a single frequency through a metal specimen with a sub-millimeter anomaly and collecting the transmitted signal at several lateral locations on the opposite side (the observation plane) of the specimen. The distance between the anomaly and the observation plane falls in the quasi Fresnel diffraction regime. The use of transmitted signals is essential to evaluate the phase shift due to the anomaly, which contains information about the substance in the anomaly. We have developed a seven-layered CNN to analyze the acoustic signal in the frequency domain. The CNN takes spectrograms representing the change in the amplitude and phase of the Fourier transform over the lateral position on the observation plane as input and classifies the anomaly into nine classes in association with the lateral location of the anomaly relative to the probing signal and the material of the anomaly. The CNN performed excellently demonstrating the validation accuracy as high as 99.9%. This result clearly demonstrates CNN’s ability to extract features in the input signal that are undetectable to humans. |
| first_indexed | 2024-03-09T22:15:16Z |
| format | Article |
| id | doaj.art-5d78ff9b543b497fa12ab1f23708a8fe |
| institution | Directory Open Access Journal |
| issn | 2571-631X |
| language | English |
| last_indexed | 2024-03-09T22:15:16Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Vibration |
| spelling | doaj.art-5d78ff9b543b497fa12ab1f23708a8fe2023-11-23T19:24:45ZengMDPI AGVibration2571-631X2022-08-015353055610.3390/vibration5030030Deep Machine Learning for Acoustic Inspection of Metallic MediumBrittney Jarreau0Sanichiro Yoshida1Emily Laprime2Integrated Science and Technology, Southeastern Louisiana University, Hammond, LA 70402, USADepartment of Chemistry and Physics, Southeastern Louisiana University, Hammond, LA 70402, USAEngineering and Applied Sciences, University of New Orleans, Lakeshore Dr., New Orleans, LA 70108, USAAcoustic non-destructive testing is widely used to detect signs of damage. However, an experienced technician is typically responsible for interpreting the result, and often the evaluation varies depending on the technician’s opinion. The evaluation is especially challenging when the acoustic signal is analyzed in the near field as Fresnel range diffraction complicates the data. In this study, we propose a Convolutional Neural Network (CNN) algorithm to detect anomalies bearing in mind its future application to micro-scale specimens such as biomedical materials. Data are generated by emitting a continuous sound wave at a single frequency through a metal specimen with a sub-millimeter anomaly and collecting the transmitted signal at several lateral locations on the opposite side (the observation plane) of the specimen. The distance between the anomaly and the observation plane falls in the quasi Fresnel diffraction regime. The use of transmitted signals is essential to evaluate the phase shift due to the anomaly, which contains information about the substance in the anomaly. We have developed a seven-layered CNN to analyze the acoustic signal in the frequency domain. The CNN takes spectrograms representing the change in the amplitude and phase of the Fourier transform over the lateral position on the observation plane as input and classifies the anomaly into nine classes in association with the lateral location of the anomaly relative to the probing signal and the material of the anomaly. The CNN performed excellently demonstrating the validation accuracy as high as 99.9%. This result clearly demonstrates CNN’s ability to extract features in the input signal that are undetectable to humans.https://www.mdpi.com/2571-631X/5/3/30material testingacousticsconvolutional neural networkmachine learning |
| spellingShingle | Brittney Jarreau Sanichiro Yoshida Emily Laprime Deep Machine Learning for Acoustic Inspection of Metallic Medium Vibration material testing acoustics convolutional neural network machine learning |
| title | Deep Machine Learning for Acoustic Inspection of Metallic Medium |
| title_full | Deep Machine Learning for Acoustic Inspection of Metallic Medium |
| title_fullStr | Deep Machine Learning for Acoustic Inspection of Metallic Medium |
| title_full_unstemmed | Deep Machine Learning for Acoustic Inspection of Metallic Medium |
| title_short | Deep Machine Learning for Acoustic Inspection of Metallic Medium |
| title_sort | deep machine learning for acoustic inspection of metallic medium |
| topic | material testing acoustics convolutional neural network machine learning |
| url | https://www.mdpi.com/2571-631X/5/3/30 |
| work_keys_str_mv | AT brittneyjarreau deepmachinelearningforacousticinspectionofmetallicmedium AT sanichiroyoshida deepmachinelearningforacousticinspectionofmetallicmedium AT emilylaprime deepmachinelearningforacousticinspectionofmetallicmedium |