Study on intelligent and visualization method of ultrasonic testing of composite materials based on deep learning
•The DiMP object tracking model is improved by using Wasserstein distance to realize the visual localization of the ultrasonic probe.•A 1DCNN classification network is designed to classify one-dimensional ultrasonic signals.•A data connection is proposed to make the two models work together.•The pro...
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| Vydáno v: | Applied acoustics Ročník 207; s. 109363 |
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| Jazyk: | angličtina |
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Elsevier Ltd
01.05.2023
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| ISSN: | 0003-682X, 1872-910X |
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| Abstract | •The DiMP object tracking model is improved by using Wasserstein distance to realize the visual localization of the ultrasonic probe.•A 1DCNN classification network is designed to classify one-dimensional ultrasonic signals.•A data connection is proposed to make the two models work together.•The proposed method is embedded in the control framework of ultrasonic A-scan equipment, and preliminary engineering applications are carried out.
Intelligent ultrasonic testing technology of composite materials can greatly reduce the dependence on people and improve the efficiency of ultrasonic testing. The combination of ultrasonic testing and visual positioning technology can realize strong robust visual interpretation of ultrasonic testing results. In this paper, the DiMP tracking model is improved by using the Wasserstein distance, and the intelligent tracking and positioning of ultrasonic probe is realized. At the same time, an ultrasonic signal classification network based on 1DCNN depth neural network is built to realize the intelligent detection of ultrasonic signals, and an effective data connection mode is designed to make the two networks work together, so that the intelligent interpretation and visual display of internal defects of composite materials can be realized. The experimental results show that the interpretation accuracy of the method proposed in this paper reaches 98.74%, and the Kappa coefficient reaches 0.97. The comparison results with other models show that the improved model in this paper is more excellent, and the AUC and Precision values are increased by 6.4% and 8.32% respectively compared with the benchmark. |
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| AbstractList | •The DiMP object tracking model is improved by using Wasserstein distance to realize the visual localization of the ultrasonic probe.•A 1DCNN classification network is designed to classify one-dimensional ultrasonic signals.•A data connection is proposed to make the two models work together.•The proposed method is embedded in the control framework of ultrasonic A-scan equipment, and preliminary engineering applications are carried out.
Intelligent ultrasonic testing technology of composite materials can greatly reduce the dependence on people and improve the efficiency of ultrasonic testing. The combination of ultrasonic testing and visual positioning technology can realize strong robust visual interpretation of ultrasonic testing results. In this paper, the DiMP tracking model is improved by using the Wasserstein distance, and the intelligent tracking and positioning of ultrasonic probe is realized. At the same time, an ultrasonic signal classification network based on 1DCNN depth neural network is built to realize the intelligent detection of ultrasonic signals, and an effective data connection mode is designed to make the two networks work together, so that the intelligent interpretation and visual display of internal defects of composite materials can be realized. The experimental results show that the interpretation accuracy of the method proposed in this paper reaches 98.74%, and the Kappa coefficient reaches 0.97. The comparison results with other models show that the improved model in this paper is more excellent, and the AUC and Precision values are increased by 6.4% and 8.32% respectively compared with the benchmark. |
| ArticleNumber | 109363 |
| Author | He, Weifeng Pei, Binbin Xu, Haojun Li, Caizhi Wei, Xiaolong Guo, Hanyi Hu, Qichun |
| Author_xml | – sequence: 1 givenname: Qichun surname: Hu fullname: Hu, Qichun organization: Science and Technology on Plasma Dynamics Laboratory, Air Force Engineering University, Xi’an, Shaanxi 710038, China – sequence: 2 givenname: Xiaolong orcidid: 0000-0001-6119-1400 surname: Wei fullname: Wei, Xiaolong email: wei18892022001@163.com organization: Science and Technology on Plasma Dynamics Laboratory, Air Force Engineering University, Xi’an, Shaanxi 710038, China – sequence: 3 givenname: Hanyi surname: Guo fullname: Guo, Hanyi organization: Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China – sequence: 4 givenname: Haojun surname: Xu fullname: Xu, Haojun organization: Science and Technology on Plasma Dynamics Laboratory, Air Force Engineering University, Xi’an, Shaanxi 710038, China – sequence: 5 givenname: Caizhi surname: Li fullname: Li, Caizhi organization: Science and Technology on Plasma Dynamics Laboratory, Air Force Engineering University, Xi’an, Shaanxi 710038, China – sequence: 6 givenname: Weifeng surname: He fullname: He, Weifeng organization: Science and Technology on Plasma Dynamics Laboratory, Air Force Engineering University, Xi’an, Shaanxi 710038, China – sequence: 7 givenname: Binbin surname: Pei fullname: Pei, Binbin organization: Science and Technology on Plasma Dynamics Laboratory, Air Force Engineering University, Xi’an, Shaanxi 710038, China |
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| Cites_doi | 10.1080/15376494.2020.1759164 10.1109/CVPR42600.2020.00721 10.1162/neco.1997.9.8.1735 10.1109/TPAMI.2019.2957464 10.1109/EIT.2017.8053371 10.1177/1687814020913761 10.1007/s10921-010-0086-0 10.1109/CVPR42600.2020.00661 10.1109/CVPR.2019.00479 10.1109/CVPR42600.2020.00716 10.1016/j.neucom.2016.11.066 10.1109/ULTSYM.2017.8091947 |
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| Keywords | PrDiMP Deep learning Composite materials D3S DiMP Non-destructive testing Ultrasonic flaw detection ATOM KCF |
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| References_xml | – year: 2017 ident: b0030 article-title: Ultrasonic signal classification and imaging system for composite materials via deep convolutional neural networks publication-title: Neurocomputing – volume: 30 start-page: 20 year: 2011 end-page: 28 ident: b0015 article-title: Automatic Defect Classification in Ultrasonic NDT Using Artificial Intelligence publication-title: J Nondestr Eval – reference: Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Houlsby N. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. doi:10.48550/arXiv.2010.11929 (2020). – reference: Wang B, Saniie J. Ultrasonic flaw detection based on temporal and spectral signals applied to neural network. In: 2017 IEEE International Ultrasonics Symposium (IUS). – reference: Danelljan M, Gool LV, Timofte R. 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| SubjectTerms | Composite materials Deep learning Non-destructive testing Ultrasonic flaw detection |
| Title | Study on intelligent and visualization method of ultrasonic testing of composite materials based on deep learning |
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