Effect of radiographic film quality on SMAW weld defect detection in SA 240 Gr 316L pressure vessels
Abstract
This study shows that radiographic film quality significantly affects the accuracy of SMAW weld defect detection in SA 240 Gr 316L pressure vessels. Radiographic evaluations using both the Panoramic and Internal Source Techniques revealed variations in film density, with the Internal Source Technique achieving slightly higher density values (2.78–3.15) than the Panoramic Technique (2.50–2.88). A sensitivity level of 1.66% was achieved, allowing for the identification of slag inclusions (42 mm and 10 mm) and porosity defects (8 mm). Defects exceeding ASME Section V acceptance criteria required re-welding and re-evaluation. Several factors influencing radiographic quality were identified, including radiation source activity, exposure duration, and film processing conditions. Optimizing these parameters is essential for ensuring reliable defect detection and maintaining pressure vessel integrity. Future research could focus on advanced image processing techniques or digital radiography.
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K. Sotoodeh, “Storage tank essential design considerations,†in Storage Tanks Selection, Design, Testing, Inspection, and Maintenance, Elsevier, 2024, pp. 101–170. doi: 10.1016/B978-0-443-23909-0.00004-7.
S. Gulfam, K. Ahmed, A. Y. Mian, and Z. Ahmed, “Analyzing buckling phenomena in an external pressure vessel: Implications for design and safety,†Fusion Eng. Des., vol. 212, p. 114852, Mar. 2025, doi: 10.1016/j.fusengdes.2025.114852.
S. Liu, S. Zhao, Z. Wu, Z. Wei, and G. Hu, “Study of microstructure and mechanical properties of SA553/SA537 steels joints using multi-pass welding for pressure vessels application,†Int. J. Press. Vessel. Pip., vol. 209, p. 105186, Jun. 2024, doi: 10.1016/j.ijpvp.2024.105186.
D. E. S. Franklin, S. J. Vijay, S. Mohanasundaram, I. Kantharaj, and D. J. H. Gabriel, “A study on plasma keyhole welding of stainless steel grade 316,†Mater. Today Proc., vol. 47, pp. 6908–6912, 2021, doi: 10.1016/j.matpr.2021.05.187.
M. Wang, K. Guo, Y. Wei, J. Chen, C. Cao, and Z. Tong, “Failure analysis of cracking in the thin-walled pressure vessel of electric water heater,†Eng. Fail. Anal., vol. 143, p. 106913, Jan. 2023, doi: 10.1016/j.engfailanal.2022.106913.
W. Nsengiyumva, S. Zhong, J. Lin, Q. Zhang, J. Zhong, and Y. Huang, “Advances, limitations and prospects of nondestructive testing and evaluation of thick composites and sandwich structures: A state-of-the-art review,†Compos. Struct., vol. 256, p. 112951, Jan. 2021, doi: 10.1016/j.compstruct.2020.112951.
J. R. Deepak, V. K. Bupesh Raja, D. Srikanth, H. Surendran, and M. M. Nickolas, “Non-destructive testing (NDT) techniques for low carbon steel welded joints: A review and experimental study,†Mater. Today Proc., vol. 44, pp. 3732–3737, 2021, doi: 10.1016/j.matpr.2020.11.578.
I. Segovia RamÃrez, F. P. GarcÃa Márquez, and M. Papaelias, “Review on additive manufacturing and non-destructive testing,†J. Manuf. Syst., vol. 66, pp. 260–286, Feb. 2023, doi: 10.1016/j.jmsy.2022.12.005.
L. Yenumula et al., “Radiographic evaluation of gas tungsten arc welded joints used in nuclear applications by X- and gamma-rays,†NDT E Int., vol. 102, pp. 144–152, Mar. 2019, doi: 10.1016/j.ndteint.2018.11.017.
R. L. Crane, “7.10 Radiographic Inspection of Composite Materials,†in Comprehensive Composite Materials II, Elsevier, 2018, pp. 167–194. doi: 10.1016/B978-0-12-803581-8.03928-X.
K. Apte and S. Bhide, “Basics of radiation,†in Advanced Radiation Shielding Materials, Elsevier, 2024, pp. 1–23. doi: 10.1016/B978-0-323-95387-0.00013-3.
A. A. Oglat, “Comparison of X-ray films in term of kVp, mA, exposure time and distance using Radiographic Chest Phantom as a radiation quality,†J. Radiat. Res. Appl. Sci., vol. 15, no. 4, p. 100479, Dec. 2022, doi: 10.1016/j.jrras.2022.100479.
M. Stewart, “History and organization of codes,†in Surface Production Operations, Elsevier, 2021, pp. 19–60. doi: 10.1016/B978-0-12-803722-5.00002-1.
R. Zhang, D. Liu, Q. Bai, L. Fu, J. Hu, and J. Song, “Research on X-ray weld seam defect detection and size measurement method based on neural network self-optimization,†Eng. Appl. Artif. Intell., vol. 133, p. 108045, Jul. 2024, doi: 10.1016/j.engappai.2024.108045.
T. Liu, P. Zheng, J. Bao, and H. Chen, “A state-of-the-art survey of welding radiographic image analysis: Challenges, technologies and applications,†Measurement, vol. 214, p. 112821, Jun. 2023, doi: 10.1016/j.measurement.2023.112821.
R. Budhu, B. P. Nkosi, and T. E. Khoza, “Radiologists’ perceptions of knowledge and training required by radiographers in the interpretation of radiographic images: An explorative study in KwaZulu-Natal province, South Africa,†J. Med. Imaging Radiat. Sci., vol. 54, no. 3, pp. 457–464, Sep. 2023, doi: 10.1016/j.jmir.2023.06.001.
J. Soltes, L. Viererbl, V. Klupak, M. Vins, and B. Michalcova, “Performance of Self-developing Radiography Films in LVR-15’s Neutron Beams,†Phys. Procedia, vol. 88, pp. 237–242, 2017, doi: 10.1016/j.phpro.2017.06.033.
“ASME Sec.â€
M. D. L. Balela, P. M. C. Rheinhardt, N. N. T. Tanggol, and J. C. Urriquia, “Silver recovery from waste radiographic film using oxalic acid,†Mater. Today Proc., vol. 33, pp. 1993–1996, 2020, doi: 10.1016/j.matpr.2020.06.378.
R. Singh, “Radiography,†in Applied Welding Engineering, Elsevier, 2020, pp. 311–330. doi: 10.1016/B978-0-12-821348-3.00023-9.
W. Du, H. Shen, J. Fu, G. Zhang, and Q. He, “Approaches for improvement of the X-ray image defect detection of automobile casting aluminum parts based on deep learning,†NDT E Int., vol. 107, p. 102144, Oct. 2019, doi: 10.1016/j.ndteint.2019.102144.
T. Tani and T. Naka, “Dark matter event in nuclear emulsions: Comparative analysis by time-resolved photoconductivity of silver halide grains,†Nucl. Instruments Methods Phys. Res. Sect. A Accel. Spectrometers, Detect. Assoc. Equip., vol. 1046, p. 167633, Jan. 2023, doi: 10.1016/j.nima.2022.167633.
M. Zaky, S. Syukran, and A. Azwar, “Inspeksi sambungan las pada pipa steam generator menggunakan metode radiography teknik panoramic (studi kasus di PT. Tachi Jino),†J. POLIMESIN, vol. 15, no. 2, p. 50, 2017, doi: 10.30811/jpl.v15i2.374.
F. M. Suyama, M. R. Delgado, R. Dutra da Silva, and T. M. Centeno, “Deep neural networks based approach for welded joint detection of oil pipelines in radiographic images with Double Wall Double Image exposure,†NDT E Int., vol. 105, pp. 46–55, Jul. 2019, doi: 10.1016/j.ndteint.2019.05.002.
D. Radi, M. E. A. Abo-Elsoud, and F. Khalifa, “Accurate segmentation of weld defects with horizontal shapes,†NDT E Int., vol. 126, p. 102599, Mar. 2022, doi: 10.1016/j.ndteint.2021.102599.
V. Vasan, N. V. Sridharan, R. J. Balasundaram, and S. Vaithiyanathan, “Ensemble-based deep learning model for welding defect detection and classification,†Eng. Appl. Artif. Intell., vol. 136, p. 108961, Oct. 2024, doi: 10.1016/j.engappai.2024.108961.
H. Yu, X. Li, H. Xie, X. Li, and C. Hou, “Improving the industrial defect recognition in radiographic testing by pre-training on medical radiographs,†NDT E Int., vol. 149, p. 103260, Jan. 2025, doi: 10.1016/j.ndteint.2024.103260.
E. Mirmahdi, R. Khamedi, D. Afshari, and M. Khosravi, “Investigating the effects of defects and the effect of geometric anisotropy in stainless steel pipes: phased array ultrasonic test, SH-wave,†J. Pipeline Sci. Eng., vol. 3, no. 4, p. 100140, Dec. 2023, doi: 10.1016/j.jpse.2023.100140.
DOI: http://dx.doi.org/10.30811/jpl.v23i2.6172
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