Research on the Acoustic Emission Source Localization Methodology in Composite Materials based on Artificial Intelligence
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917714736447488 |
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| author | Won, Jongick Oh, Hyuntaik Sakong, Jae |
| author_facet | Won, Jongick Oh, Hyuntaik Sakong, Jae |
| contents | In this study, methodology of acoustic emission source localization in composite materials based on artificial intelligence was presented. Carbon fiber reinforced plastic was selected for specimen, and acoustic emission signal were measured using piezoelectric devices. The measured signal was wavelet-transformed to obtain scalograms, which were used as training data for the artificial intelligence model. AESLNet(acoustic emission source localization network), proposed in this study, was constructed convolutional layers in parallel due to anisotropy of the composited materials. It is regression model to detect the coordinates of acoustic emission source location. Hyper-parameter of network has been optimized by Bayesian optimization. It has been confirmed that network can detect location of acoustic emission source with an average error of 3.02mm and a resolution of 20mm. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_05405 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Research on the Acoustic Emission Source Localization Methodology in Composite Materials based on Artificial Intelligence Won, Jongick Oh, Hyuntaik Sakong, Jae Sound Audio and Speech Processing Data Analysis, Statistics and Probability In this study, methodology of acoustic emission source localization in composite materials based on artificial intelligence was presented. Carbon fiber reinforced plastic was selected for specimen, and acoustic emission signal were measured using piezoelectric devices. The measured signal was wavelet-transformed to obtain scalograms, which were used as training data for the artificial intelligence model. AESLNet(acoustic emission source localization network), proposed in this study, was constructed convolutional layers in parallel due to anisotropy of the composited materials. It is regression model to detect the coordinates of acoustic emission source location. Hyper-parameter of network has been optimized by Bayesian optimization. It has been confirmed that network can detect location of acoustic emission source with an average error of 3.02mm and a resolution of 20mm. |
| title | Research on the Acoustic Emission Source Localization Methodology in Composite Materials based on Artificial Intelligence |
| topic | Sound Audio and Speech Processing Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2407.05405 |