Experimental Demonstration of 450kVp MesoFocus X-Ray Radiography for Rail Weld Inspection
Source abstract
Abstract
Rail welds are critical points of potential failure in railway infrastructure, yet conventional inspection methods such as ultrasonic testing and Ir-192 gamma radiography exhibit limited sensitivity to small internal flaws in thick rail sections. This study investigates the application of a 450 kVp MesoFocus X-ray source coupled with a Varex 4343HE flat-panel detector for non-destructive evaluation of rail welds. Experimental radiographs were acquired from a reference rail containing artificial flaws ranging from 0.5 mm to 3.0 mm in diameter. Image quality metrics, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and basic spatial resolution, were analysed to quantify image performance. SNR measurements were performed using multiple lead filters of varying thicknesses; the 1 mm filter provided the optimal SNR. Frame averaging enhanced image quality up to approximately 200 frames, beyond which SNR plateaued, while both SNR and CNR decreased with increasing material thickness. Flaws as small as 0.5 mm were resolvable through 42 mm of steel, whereas the detectability of 1–3 mm flaws decreased in the 74-mm -thick rail head. These results establish the detection limits of 450 kV X-ray rail-weld radiography and provide quantitative guidance for optimizing deployable in-track inspection systems aimed at improving railway safety.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Welding Techniques and Residual Stresses · Advanced X-ray and CT Imaging · Advanced X-ray Imaging Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (32)
Show source-provided references
1. Saita, K., Karimine, K., Ueda, M., Iwano, K., Yamamoto, T., Hiroguchi, K.: Trends in rail welding technologies and our future approach. Nippon steel & sumitomo metal technical report 105, 84–92 (2013)
2. Cannon, D., Edel, K.O., Grassie, S., Sawley, K.: Rail defects: an overview. Fatigue & Fracture of Engineering Materials & Structures 26(10), 865–886 (2003)10.1046/j.1460-2695.2003.00693.x ↗
3. Mićić, M., Brajović, L., Lazarević, L., Popović, Z.: Inspection of RCF rail defects–Review of NDT methods. Mechanical Systems and Signal Processing 182, 109568 (2023)10.1016/j.ymssp.2022.109568 ↗
4. Liu, X., Saat, M.R., Barkan, C.P.: Analysis of causes of major train derailment and their effect on accident rates. Transportation Research Record: Journal of the Transportation Research Board 2289(1), 154–163 (2012)10.3141/2289-20 ↗
5. FRA, "Train Accidents and Rates: 2024 Safety Data Report," U.S. Department of Transportation, Washington, D.C., 2024. Accessed: February 11, 2025. Available: https://safetydata.fra.dot.gov/
6. N. T. S. Board. "NTSB Investigation: RRD24FR001." National Transportation Safety Board. https://www.ntsb.gov/investigations/Pages/RRD24FR001.aspx Accessed 13 June 2025
7. Delgado, D., Aktas, C.B.: Resilience of Rail Infrastructure in the U.S. Northeast Corridor. Procedia Engineering 145, 356–363 (2016). https://doi.org/10.1016/j.proeng.2016.04.08910.1016/j.proeng.2016.04.089 ↗
8. Van Drunen, G., Cecco, V.S.: Recognizing limitations in eddy-current testing. NDT International 17(1), 9–17 (1984). https://doi.org/10.1016/0308-9126(84)90108-110.1016/0308-9126(84)90108-1 ↗
9. AbdAlla, A.N., Faraj, M.A., Samsuri, F., Rifai, D., Ali, K., Al-Douri, Y.: Challenges in improving the performance of eddy current testing. Measurement and Control 52(1–2), 46–64 (2019)10.1177/0020294018801382 ↗
10. Olisa, S.C., Khan, M.A., Starr, A.: Review of current guided wave ultrasonic testing (GWUT) limitations and future directions. Sensors 21(3), 811 (2021)10.3390/s21030811 ↗
11. Gupta, M., Khan, M.A., Butola, R., Singari, R.M.: Advances in applications of Non-Destructive Testing (NDT): A review. Advances in Materials and Processing Technologies 8(2), 2286–2307 (2022)10.1080/2374068X.2021.1909332 ↗
12. N. T. S. Board, "Derailment of CSX Coal Train on the Old Main Line Subdivision, Ellicott City, Maryland, August 20, 2012," Washington, DC, DCA12MR009, 2013. Accessed: 13 June 2025. Available: https://www.ntsb.gov/investigations/Pages/DCA12MR005.aspx
13. Barnatt, N., Jack, A.: Safety analysis in a modern railway setting. Safety Science 110, 177–182 (2018). https://doi.org/10.1016/j.ssci.2018.08.00510.1016/j.ssci.2018.08.005 ↗
14. Russo, P.: Handbook of X-ray imaging: physics and technology. CRC press (2017)10.1201/9781351228251 ↗
15. B. I. o. N.-D. Testing. "Radiography – What is NDT?" https://www.bindt.org/What-is-NDT/Radiography/ (accessed.
16. Chmieliński, Z., et al.: Feasibility Study of Radiographic Rail Inspections Using a Compact X-ray Tube. Journal of Nondestructive Evaluation 44(2), 1–9 (2025)10.1007/s10921-025-01193-x ↗
17. Federal Railroad Administration, Office of Research, Development, and Technology.: Broad Agency Announcement – BAA 2025: Appendix B – Research Topics. FRA-TR-001: Advanced Weld Inspection (2025). https://railroads.dot.gov/research-development/program-areas/research-and-development-funding/broad-agency-announcement
18. Kersting, T., Schönartz, N., Oesterlein, L., Liessem, A.: High end inspection by filmless radiography on LSAW large diameter pipes. NDT & E International 43(3), 206–209 (2010)10.1016/j.ndteint.2009.11.004 ↗
19. Klimenov, V. A., Buller, A., Moskalev, Y., Chakhlov, S., Shteyn, M.: Mobile digital radiography system for nondestructive testing of large diameter pipelines. In: 18th World Conference on Non-Destructive Testing: Proceedings, Durban, 16–20 April 2012
20. Pardikar, R.J.: Digital radiography and computed radiography for enhancing the quality and productivity of weldments in boiler components. In: 17th World Conference on Nondestructive Testing, Shanghai, 25–28 Oct 2008. https://www.academia.edu/download/88654183/596.pdf
21. Hellmann, D.C.: 450 kVp high dynamic range radiographic inspection of rail welds. Master's Thesis, Nuclear Engineering. University of Tennessee, Knoxville (2023). https://trace.tennessee.edu/utk_gradthes/9959
22. U. P. Railroad. "Track Standard Drawings: Rail Sections (Commonly Used)." Union Pacific Engineering Department. https://www.up.com/emp/engineering/mapcontent/standards/track%20standard%20drawings/0724.pdf (accessed 2/28/2026).
23. Berger, M.J., Hubbell, J.H., Seltzer, S.M., Coursey, J.S., Zucker, D.S.: XCOM: Photon cross section database (version 1.2). National Institute of Standards and Technology (NIST), Gaithersburg (1999)
24. V. Imaging. "DC-Thor HE Photon Counting Detector." https://www.vareximaging.com/solutions/dc-thor-he/ (accessed June 15, 2025.
25. Martz, H.E., Logan, C.M., Schneberk, D.J., Shull, P.J.: X-ray Imaging: fundamentals, industrial techniques and applications. CRC Press (2016)10.1201/9781315375199 ↗
26. Jaenisch, G.-R., Ewert, U., Jechow, M.: Scatter radiation in radiography. ECNDT, Moscow (2010)
27. Bengtsson, A., Hill, D., Shortiss, K., Rippe, L., Kröll, S.: Comparison of contrast-to-noise ratios of different detection methods in ultrasound optical tomography. Biomedical Optics Express 13(9), 4834–4850 (2022)10.1364/BOE.457075 ↗
28. ISO 17636–2:2022(En). Non-Destructive Testing of Welds—Radiographic Testing—Part 2: X- and Gamma-Ray Techniques with Digital Detectors, ISO-17636–2, 2022. [Online]. Available: https://www.iso.org/obp/ui/#iso:std:iso:17636:-2:ed-2:v2:en
29. ASTM International.: Standard practice for determining image unsharpness and basic spatial resolution in radiography and radioscopy. ASTM E2002-22, West Conshohocken (2022)
30. Hena, B., Wei, Z., Castanedo, C.I., Maldague, X.: Deep learning neural network performance on NDT digital X-ray radiography images: analyzing the impact of image quality parameters—an experimental study. Sensors 23(9), 4324 (2023)10.3390/s23094324 ↗
31. Hou, W., Wei, Y., Guo, J., Jin, Y., Zhu, C.A.: Automatic detection of welding defects using deep neural network. J. Phys. Conf. Ser. 933, 012006. IOP Publishing (2018). https://iopscience.iop.org/article/10.1088/1742-6596/933/1/012006/meta10.1088/1742-6596/933/1/012006 ↗
32. Jha, S.B., Babiceanu, R.F.: Deep CNN-based visual defect detection: Survey of current literature. Computers in Industry 148, 103911 (2023). https://doi.org/10.1016/j.compind.2023.10391110.1016/j.compind.2023.103911 ↗