The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Acoustic emission method for defect detection and identification in carbon steel welded joints
Open accessAbstract
Detecting welding defects in industrial equipment (welded joints and built-up structures) is a key aspect in evaluating the probability of failure in different situations. Acoustic emission (AE) is an effective non-destructive detecting technique, and can be a promising application for welding defect detection. This work presents a systematic experimental investigation on using AE technique for detecting and classifying different weld defects in carbon steel joint material. Four certified carbon steel samples were used in this study. A defect free control sample was used as the reference and three samples with induced defects, namely slag, porosity and crack. A pencil lead break (PLB) test was used to generate simulated AE sources on one side of the joint whereas the AE sensor was mounted on the other side to capture AE signals. A total of four experimental arrangements were used to investigate the effect of propagating distance (sensor to source distance) on the ability of AE to detect and identify defects in welds. For each of these arrangements, AE features such as peak amplitude, rise time, decay time, duration, and count numbers along with statistical features such as AE energy, root mean square (RMS) were extracted and analysed. Also, frequency analysis using FFT and wavelet transform were investigated for each weld test specimen for all arrangements. The results show that AE energy, peak amplitude and RMS value can be used to automatically detect and identify the presence of a defect in carbon steel welds. It is concluded that AE has a considerable potential in use in welding inspection to assess the overall structural health and identify defects that can significantly reduce the strength and reliability of welded material and consequently reduce the risk of component's failure.
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“However, the above traditional machine learning-based defect detection methods have low detection accuracy and cannot meet the needs of industry.”
From A deep learning model for steel surface defect detection (2024) · cites this paper as [6]
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Cites
Defect identification in friction stir welding using discrete wavelet analysis
Kumar, U., Yadav, I., Kumari, S., Kumari, K., Ranjan, N. S., Kesharwani, R. K., Jain, R., Kumar, S., Pal, S., Chakravarty, D., et al. (2015) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2015.02.001 · Cited by 76
Indentation testing and its acoustic emission response: applications and emerging trends
Faisal, N. H., Ahmed, R., & Reuben, R. L. (2011) · International Materials Reviews · doi:10.1179/1743280410y.0000000004 · Cited by 70
Self-citationAcoustic emission detection of fatigue damage in cruciform welded joints
Yu, J., Ziehl, P. H., Matta, F., & Pollock, A. A. (2013) · Journal of Constructional Steel Research · doi:10.1016/j.jcsr.2013.03.017 · Cited by 55
Screening for Welding Defects Using Acoustic Emission Technique
Aboali, A., El-Shaib, M. N., Sharara, A., & Shehadeh, M. (2014) · Advanced materials research · doi:10.4028/www.scientific.net/amr.1025-1026.7 · Cited by 13
Self-citationEvaluation on defect in the weld of stainless steel materials using nondestructive technique
Lee, J. K., Bae, D. S., Lee, S. P., & Lee, J. H. (2014) · Fusion Engineering and Design · doi:10.1016/j.fusengdes.2013.12.026 · Cited by 9
Acoustic Emission Studies on Weld Bead Defects in Nuclear Grade SS 316L Materials
Ranganayakulu, S. V., Shiva Raju, J., Kuchedludu, A., & Ramesh Kumar, B. (2014) · Open Journal of Acoustics · doi:10.4236/oja.2014.43012 · Cited by 6
Halim, Z. A., Jamaludin, N., Syarif, J., & Yahya, S. Y. S. (2015) · Journal of Mechanical Science and Technology · doi:10.1007/s12206-015-0328-2 · Cited by 4
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Yang, J., Li, S., Wang, Z., Dong, H., Wang, J., & Tang, S. (2020) · Materials · doi:10.3390/ma13245755 · Cited by 488
A review on wire arc additive manufacturing: Monitoring, control and a framework of automated system
Xia, C., Pan, Z., Polden, J., Li, H., Xu, Y., Chen, S., & Zhang, Y. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.08.008 · Cited by 460
Shaloo, M., Schnall, M., Klein, T., Huber, N. W., & Reitinger, B. (2022) · Materials · doi:10.3390/ma15103697 · Cited by 147
A deep learning model for steel surface defect detection
Li, Z., Wei, X., Hassaballah, M., Li, Y., & Jiang, X. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01180-7 · Cited by 122
Machine learning for ultrasonic nondestructive examination of welding defects: A systematic review
Sun, H., Ramuhalli, P., & Jacob, R. E. (2022) · Ultrasonics · doi:10.1016/j.ultras.2022.106854 · Cited by 129
Metadata
- Authors
- Droubi, M. G., Faisal, N. H., Orr, F., Steel, J. A., & El-Shaib, M.
- Affiliations
- Robert Gordon University (GB)
- Arab Academy for Science, Technology, and Maritime Transport (EG)
- Corresponding author
- Droubi, Mohamad G.
- Venue
- Journal of Constructional Steel Research
- Publisher
- Elsevier BV
- Published
- 2017-07
- Volume
- 134
- Pages
- 28-37
- ISSN
- 0143-974X (ISSN-L)
- OpenAlex
- W2600553928
- Cited by
- 85
- References
- 7
- Citations by year
- 2026: 13 · 2025: 8 · 2024: 8 · 2023: 9 · 2022: 10 · 2021: 13 · 2020: 8 · 2019: 7 · 2018: 7 · 2017: 2
- FWCI
- 5.10
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc-nd
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Journal of Constructional Steel Research (publishedVersion)
- Open Access Institutional Repository at Robert Gordon University (Robert Gordon University) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Physical Sciences › Engineering › Mechanics of Materials › Ultrasonics and Acoustic Wave Propagation
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Engineering and Materials Analysis
- Keywords
- acoustic emission, weld defect detection, defect classification, slag inclusions, porosity, weld cracks, pencil lead break test, acoustic emission energy, peak amplitude, wavelet transform, frequency analysis, structural health monitoring
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- The paper
- Droubi, M. G., Faisal, N. H., Orr, F., Steel, J. A., & El-Shaib, M. (2017). Acoustic emission method for defect detection and identification in carbon steel welded joints. Journal of Constructional Steel Research. https://doi.org/10.1016/j.jcsr.2017.03.012
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
