Multi-Branch Deep Fusion Network-Based Automatic Detection of Weld Defects Using Non-Destructive Ultrasonic Test
This study introduces a deep learning engine designed for the non-destructive automatic detection of defects within weld beads. A 1D waveform ultrasound signal was collected using an A-scan pulser receiver to gather defect signals from inside the weld bead. We established 5,108 training datasets and...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10286042/ |
| _version_ | 1797653999971729408 |
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| author | Kyeeun Kim Keo Sik Kim Hyoung-Jun Park |
| author_facet | Kyeeun Kim Keo Sik Kim Hyoung-Jun Park |
| author_sort | Kyeeun Kim |
| collection | DOAJ |
| description | This study introduces a deep learning engine designed for the non-destructive automatic detection of defects within weld beads. A 1D waveform ultrasound signal was collected using an A-scan pulser receiver to gather defect signals from inside the weld bead. We established 5,108 training datasets and 500 test datasets for five pass/fail labels in this study. We developed a multi-branch deep fusion network (MBDFN) model that independently trains 1D-CNN for local pattern learning within a sequence and 2D-CNN for spatial feature extraction and then combines them in an ensemble method, achieving a classification accuracy of 92.2%. The resulting deep learning engine has potential applications in automatic welding robots or welding inspection systems, allowing for rapid determination of internal defects without compromising the integrity of the finished product. |
| first_indexed | 2024-03-11T16:53:00Z |
| format | Article |
| id | doaj.art-09aa27b37b0445f695159ed8c2910733 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T16:53:00Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-09aa27b37b0445f695159ed8c29107332023-10-20T23:00:36ZengIEEEIEEE Access2169-35362023-01-011111448911449610.1109/ACCESS.2023.332471710286042Multi-Branch Deep Fusion Network-Based Automatic Detection of Weld Defects Using Non-Destructive Ultrasonic TestKyeeun Kim0https://orcid.org/0000-0002-2161-3899Keo Sik Kim1https://orcid.org/0000-0002-3234-1008Hyoung-Jun Park2https://orcid.org/0000-0002-8258-3224Electronics and Telecommunications Research Institute, Daejeon, South KoreaElectronics and Telecommunications Research Institute, Daejeon, South KoreaElectronics and Telecommunications Research Institute, Daejeon, South KoreaThis study introduces a deep learning engine designed for the non-destructive automatic detection of defects within weld beads. A 1D waveform ultrasound signal was collected using an A-scan pulser receiver to gather defect signals from inside the weld bead. We established 5,108 training datasets and 500 test datasets for five pass/fail labels in this study. We developed a multi-branch deep fusion network (MBDFN) model that independently trains 1D-CNN for local pattern learning within a sequence and 2D-CNN for spatial feature extraction and then combines them in an ensemble method, achieving a classification accuracy of 92.2%. The resulting deep learning engine has potential applications in automatic welding robots or welding inspection systems, allowing for rapid determination of internal defects without compromising the integrity of the finished product.https://ieeexplore.ieee.org/document/10286042/Deep learningquality managementweldingautomatic testing |
| spellingShingle | Kyeeun Kim Keo Sik Kim Hyoung-Jun Park Multi-Branch Deep Fusion Network-Based Automatic Detection of Weld Defects Using Non-Destructive Ultrasonic Test IEEE Access Deep learning quality management welding automatic testing |
| title | Multi-Branch Deep Fusion Network-Based Automatic Detection of Weld Defects Using Non-Destructive Ultrasonic Test |
| title_full | Multi-Branch Deep Fusion Network-Based Automatic Detection of Weld Defects Using Non-Destructive Ultrasonic Test |
| title_fullStr | Multi-Branch Deep Fusion Network-Based Automatic Detection of Weld Defects Using Non-Destructive Ultrasonic Test |
| title_full_unstemmed | Multi-Branch Deep Fusion Network-Based Automatic Detection of Weld Defects Using Non-Destructive Ultrasonic Test |
| title_short | Multi-Branch Deep Fusion Network-Based Automatic Detection of Weld Defects Using Non-Destructive Ultrasonic Test |
| title_sort | multi branch deep fusion network based automatic detection of weld defects using non destructive ultrasonic test |
| topic | Deep learning quality management welding automatic testing |
| url | https://ieeexplore.ieee.org/document/10286042/ |
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