Detection of Diffusion Interlayers in Dissimilar Welded Joints in Processing Pipelines by Acoustic Emission Method
The paper considers the neural network application to detect microstructure defects in dissimilar welded joints using the acoustic emission (AE) method. The peculiarity of the proposed approach is that defect detection is carried out taking into account a priori information about the properties of t...
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2024-11-01
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| author | Vera Barat Artem Marchenkov Vladimir Bardakov Dmitrij Arzumanyan Sergey Ushanov Marina Karpova Egor Lepsheev Sergey Elizarov |
| author_facet | Vera Barat Artem Marchenkov Vladimir Bardakov Dmitrij Arzumanyan Sergey Ushanov Marina Karpova Egor Lepsheev Sergey Elizarov |
| author_sort | Vera Barat |
| collection | DOAJ |
| description | The paper considers the neural network application to detect microstructure defects in dissimilar welded joints using the acoustic emission (AE) method. The peculiarity of the proposed approach is that defect detection is carried out taking into account a priori information about the properties of the AE source and the acoustic waveguide parameters of the testing structure. Industrial process pipelines with dissimilar welded joints were studied as the testing object, and diffusion interlayers formed in fusion zones of welded joints were considered microstructure defects. The simulation of AE signals was carried out using a hybrid method: the signal waveform was determined based on a finite element model, while the amplitudes of AE hits were determined based on a physical experiment on mechanical testing of dissimilar welded joints. Measurement data from industrial process pipelines were used as noise realizations. As a result, a data sample was formed that considered the parameters of the AE source and the parameters of the acoustic waveguide with realistic noise parameters and a signal-to-noise ratio. The proposed method allows for a more accurate determination of the waveform, spectrum, and amplitude parameters of the AE signal. Greater certainty in the useful signal parameters allows for achieving a more accurate and reliable classification result. When using a backpropagation neural network, a percentage of correct classification of more than 90% was obtained for a data set in which the signal-to-noise ratio was less than (−5 dB) in 90% of cases. |
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| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-fb744e7776e24e5b9b45f36d3d198a6b2024-11-26T17:49:12ZengMDPI AGApplied Sciences2076-34172024-11-0114221054610.3390/app142210546Detection of Diffusion Interlayers in Dissimilar Welded Joints in Processing Pipelines by Acoustic Emission MethodVera Barat0Artem Marchenkov1Vladimir Bardakov2Dmitrij Arzumanyan3Sergey Ushanov4Marina Karpova5Egor Lepsheev6Sergey Elizarov7Department of Information Technologies and Computer Science, National Research University “Moscow Power Engineering Institute”, 14, Krasnokazarmennaya Street, 111250 Moscow, RussiaDepartment of Information Technologies and Computer Science, National Research University “Moscow Power Engineering Institute”, 14, Krasnokazarmennaya Street, 111250 Moscow, RussiaDepartment of Information Technologies and Computer Science, National Research University “Moscow Power Engineering Institute”, 14, Krasnokazarmennaya Street, 111250 Moscow, RussiaDepartment of Information Technologies and Computer Science, National Research University “Moscow Power Engineering Institute”, 14, Krasnokazarmennaya Street, 111250 Moscow, RussiaDepartment of Information Technologies and Computer Science, National Research University “Moscow Power Engineering Institute”, 14, Krasnokazarmennaya Street, 111250 Moscow, RussiaDepartment of Information Technologies and Computer Science, National Research University “Moscow Power Engineering Institute”, 14, Krasnokazarmennaya Street, 111250 Moscow, RussiaDepartment of Information Technologies and Computer Science, National Research University “Moscow Power Engineering Institute”, 14, Krasnokazarmennaya Street, 111250 Moscow, RussiaLLC “Interunis-IT”, 20B, Entuziastov Highway, 111024 Moscow, RussiaThe paper considers the neural network application to detect microstructure defects in dissimilar welded joints using the acoustic emission (AE) method. The peculiarity of the proposed approach is that defect detection is carried out taking into account a priori information about the properties of the AE source and the acoustic waveguide parameters of the testing structure. Industrial process pipelines with dissimilar welded joints were studied as the testing object, and diffusion interlayers formed in fusion zones of welded joints were considered microstructure defects. The simulation of AE signals was carried out using a hybrid method: the signal waveform was determined based on a finite element model, while the amplitudes of AE hits were determined based on a physical experiment on mechanical testing of dissimilar welded joints. Measurement data from industrial process pipelines were used as noise realizations. As a result, a data sample was formed that considered the parameters of the AE source and the parameters of the acoustic waveguide with realistic noise parameters and a signal-to-noise ratio. The proposed method allows for a more accurate determination of the waveform, spectrum, and amplitude parameters of the AE signal. Greater certainty in the useful signal parameters allows for achieving a more accurate and reliable classification result. When using a backpropagation neural network, a percentage of correct classification of more than 90% was obtained for a data set in which the signal-to-noise ratio was less than (−5 dB) in 90% of cases.https://www.mdpi.com/2076-3417/14/22/10546dissimilar welded jointsacoustic emissiondiffusion interlayersneural networks in acoustic emissionclassification of acoustic emission signalsacoustic waveguide modeling |
| spellingShingle | Vera Barat Artem Marchenkov Vladimir Bardakov Dmitrij Arzumanyan Sergey Ushanov Marina Karpova Egor Lepsheev Sergey Elizarov Detection of Diffusion Interlayers in Dissimilar Welded Joints in Processing Pipelines by Acoustic Emission Method Applied Sciences dissimilar welded joints acoustic emission diffusion interlayers neural networks in acoustic emission classification of acoustic emission signals acoustic waveguide modeling |
| title | Detection of Diffusion Interlayers in Dissimilar Welded Joints in Processing Pipelines by Acoustic Emission Method |
| title_full | Detection of Diffusion Interlayers in Dissimilar Welded Joints in Processing Pipelines by Acoustic Emission Method |
| title_fullStr | Detection of Diffusion Interlayers in Dissimilar Welded Joints in Processing Pipelines by Acoustic Emission Method |
| title_full_unstemmed | Detection of Diffusion Interlayers in Dissimilar Welded Joints in Processing Pipelines by Acoustic Emission Method |
| title_short | Detection of Diffusion Interlayers in Dissimilar Welded Joints in Processing Pipelines by Acoustic Emission Method |
| title_sort | detection of diffusion interlayers in dissimilar welded joints in processing pipelines by acoustic emission method |
| topic | dissimilar welded joints acoustic emission diffusion interlayers neural networks in acoustic emission classification of acoustic emission signals acoustic waveguide modeling |
| url | https://www.mdpi.com/2076-3417/14/22/10546 |
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