Abstract—Algorithms are proposed for modeling the spatial outlines of test objects and forming their images in digital radiography systems. The algorithms provide the basis for simulation models of the systems being analyzed. The simulation models are designed to substantiate the technical feasibility of monitoring objects and selecting the parameters and evaluating the characteristics of digital radiography systems. To illustrate the capabilities of the developed simulation models, the digital radiographic images of some objects have been produced, including those of reference samples of sensitivity, spatial resolution, resolving ability, and penetrating power.
This is a preview of subscription content, log in via an institution to check access.
We’re sorry, something doesn't seem to be working properly.
Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.
REFERENCES
Seeram, E., Digital Radiography: An Overview, in Digital Radiography, Singapore: Springer, 2019, pp. 1–19. https://doi.org/10.1007/978-981-13-3244-9_1
Yahaghi, E., Movafeghi, A., Rokrok, B., and Mirzapour, M., Defects detection of digital radiographic images of aircraft structure materials via geometric locally adaptive sharpening, Res. Nondestr. Eval., 2020, vol. 31, no. 2, pp. 107–115. https://doi.org/10.1080/09349847.2019.1634226
Mery, D., Computer vision technology for X-ray testing, Insight (Northampton, U.K.), 2014, vol. 56, no. 3, pp. 147–155. https://doi.org/10.1784/insi.2014.56.3.147
Khorshidi, A., Khosrowpour, B., and Hosseini, S.H., Determination of defect depth in industrial radiography imaging using MCNP code and SuperMC software, Nucl. Eng. Technol., 2019. https://doi.org/10.1016/j.net.2019.12.010
Siryabe, E., Juliac, E., Barthe, A., and Ferdinand, C., X-ray digital detector array radiology to infer sagging depths in welded assemblies, NDT & E Int., 2020, vol. 111, article no. 102238. https://doi.org/10.1016/j.ndteint.2020.102238
Du, W., Shen, H., Fu, J., Zhang, G., and He, Q., Approaches for improvement of the X-ray image defect detection of automobile casting aluminum parts based on deep learning, NDT & E Int., 2019, vol. 107, article no.102144. https://doi.org/10.1016/j.ndteint.2019.102144
Ewert, U., Zscherpel, U., and Jechow, M., Essential parameters and conditions for optimum image quality in digital radiology, in 18th World Conf.Nondestr. Test., Durban, South Africa, 2012, pp. 16–20. http://www.ndt.net/ article/wcndt2012/papers/595_wcndtfinal00594.pdf.
Osipov, S.P., Zhang, G.L., Chakhlov, S.V., Shtein, M.M., Shtein, A.M., Trinh, V.B., and Sirotyan, E., Estimation of parameters of digital radiography systems, IEEE Trans. Nucl. Sci., 2018, vol. 65, no. 10, pp. 2732–2742.
Yaffe, M.J. and Rowlands, J.A., X-ray detectors for digital radiography, Phys. Med. Biol., 1997, vol. 42, no. 1, pp. 1–39. https://doi.org/10.1088/0031-9155/42/1/001
Silvan, A.S.S., Oliveira, D.F., Gomes, C.S., Azeredo, S.R., and Lopes, R.T., Evaluation of digital detector arrays systems for industrial radiography, Braz. J. Radiat. Sci., 2019, vol. 7, no. 2A. https://doi.org/10.15392/bjrs.v7i2A.601
Oh, K., Kim, J., Kim, S., Chae, M., Lee, D., Cha, H., Lee, B., Evaluation and optimization of an image acquisition system for dual-energy cargo inspections, IEEE Trans. Nucl. Sci., 2018, vol. 65, no. 9, pp. 2657–2661.
Raad, J.A. and Kuiper, A., Industrial Radiography Image Forming Techniques, General Electric Co., Inspection Technologies, 2007. http://www.geinspectiontechnologies.com/en.
Deyhle, H., Towsyfyan, H., Biguri, A., Mavrogordato, M., Boardman, R., and Blumensath, T., Spatial resolution of a laboratory based X-Ray cone-beam laminography scanning system for various trajectories, NDT & E Int., 2020, vol. 111, article no. 102222. https://doi.org/10.1016/j.ndteint.2020.102222
ANSI N42.46. American national standard for determination of the imaging performance of X-ray and gamma-ray systems for cargo and vehicle security screening, 2008.
Khan, M., Brothers, M., and Gillis, T., Computed radiography for high resolution imaging applications of aircraft structures, in 11th Int. Symp. NDT Aerosp., Paris-Saclay, France, 2019. https://www.ndt.net/article/ a-ero2019/papers/fp_thu2c1_m_khan.pdf.
Haith, M.I., Ewert, U., Hohendorf, S., Bellon, C., Deresch, A., Huthwaite, P., Lowe, M.J.S., and Zscherpel, U., Radiographic modelling for NDE of subsea pipelines, NDT & E Int., 2017, vol. 86, pp. 113—122.https://doi.org/10.1016/j.ndteint.2016.11.006
Keller, W.J. and Pessiki, S., Enhancing radiographic imaging of cementitious materials in composite structures with photon attenuating inclusions, Res. Nondestr. Eval., 2019, vol. 30, no. 4, pp. 231–251. https://doi.org/10.1080/09349847.2018.1475585
Kumar, S., Menaka, M., and Venkatraman, B., Radiographic simulation and validation studies on weld joints of annular tanks and cylindrical tanks, IOP Conf. Ser.: Mater. Sci. Eng. IOP Publ., 2019, vol. 554, no. 1, article no. 012008. https://doi.org/10.1088/1757-899X/554/1/012008
Yao, M., Kaftandjian, V., Peterzol-Parmentier, A., Schumm, A., and Duvauchelle, P., Hybrid Monte Carlo and deterministic simulation approach for modeling a computed radiography imaging chain from X-ray exposure to optical readout, Nucl. Instrum. Methods Phys. Res.,Sect. A, 2019, vol. 941, article no. 162328. https://doi.org/10.1016/j.nima.2019.06.069
Kengyelics, S.M., Treadgold, L.A., and Davies, A.G., X-ray system simulation software tools for radiology and radiography education, Comput. Biol. Med., 2018, vol. 93, pp. 175–183. https://doi.org/10.1016/j.compbiomed.2017.12.005
Udod, V.A., Osipov, S.P., and Wang, Y., The mathematical model of image, generated by scanning digital radiography system, IOP Conf. Ser.: Mater. Sci. Eng., IOP Publ., 2017, vol. 168, no. 1, article no. 012042. https://doi.org/10.1088/1757-899X/168/1/012042
Osipov, S.P., Chakhlov, S.V., Kairalapov, D.U., and Sirot’yan, E.V., Numerical modeling of radiographic images as the basis for correctly designing digital radiography systems of large-sized objects, Russ. J. Nondestr. Test., 2019, vol. 55, no. 2, pp. 136–149. https://doi.org/10.1134/S1061830919020050
Zav’yalkin, F.M. and Osipov, S.P., Dependence of the mean value and fluctuations of the absorbed energy on the scintillator dimensions, Sov. At. Energy, 1985, vol. 59, no. 4, pp. 842–846. https://doi.org/10.1007/BF01123317
ASTM E1025. Standard practice for design, manufacture, and material grouping classification of hole-type image quality indicators (IQI) used for radiology, ASTM International, West Conshohocken, PA, 2018.
Halmshaw, R., Radiographic techniques: Principles, in Industrial Radiology, Dordrecht: Springer, 1995, pp. 111–145. https://doi.org/10.1007/978-94-011-0551-4_7
Rueckel, J., Stockmar, M., Pfeiffer, F., and Herzen, J., Spatial resolution characterization of a X-ray microCT system, Appl. Radiat. Isot., 2014, vol. 94, pp. 230–234. https://doi.org/10.1016/j.apradiso.2014.08.014
Huda, W. and Abrahams, R.B., X-ray-based medical imaging and resolution, Am. J. Roentgenol., 2015, vol. 204, no. 4, pp. W393–W397. https://doi.org/10.2214/ajr.14.13126
Du Plessis, A., Yadroitsava, I., and Yadroitsev, I., Effects of defects on mechanical properties in metal additivemanufacturing: a review focusing on X-ray tomography insights, Mater. Des., 2020, vol. 187, article no. 108385. https://doi.org/10.1016/j.matdes.2019.108385
Author information
Authors and Affiliations
Corresponding authors
Additional information
Translated by V. Potapchouck
Rights and permissions
About this article
Cite this article
Osipov, S.P., Yadrenkin, I.G., Chakhlov, S.V. et al. Simulation Modeling in Digital Radiography with Allowance for Spatial Outlines of Test Objects. Russ J Nondestruct Test 56, 647–660 (2020). https://doi.org/10.1134/S1061830920080082
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S1061830920080082