Improving the Efficiency of Defect Image Identification During Computer Decoding of Digital Radiographic Images of Welded Joints of Hazardous Production Facilities

S. A. Grigorchenko; Григорченко С. А.; V. I. Kapustin; Капустин В. И.

doi:10.31857/S0130308224120056

Improving the Efficiency of Defect Image Identification During Computer Decoding of Digital Radiographic Images of Welded Joints of Hazardous Production Facilities

作者: Grigorchenko S.A.¹, Kapustin V.I.²
隶属关系:
1. Kolomna Institute (Branch) of the Moscow Polytechnic University
2. TECHNOPROGRESS Research Centre
期: 编号 12 (2024)
页面: 59-68
栏目: Рентгеновские методы
URL: https://vestnik.nvsu.ru/0130-3082/article/view/649290
DOI: https://doi.org/10.31857/S0130308224120056
ID: 649290

如何引用文章

全文:

开放存取

##reader.subscriptionAccessGranted##
受限制的访问

订阅存取

详细

This article is devoted to improving the efficiency of flaw image identification during computer decoding of digital radiographic images. The paper studies the problem of segmentation of flaw images. Models of segmentation of flaw images on a radiographic image are studied for both manual and computer decoding. The difference between algorithms for searching and identifying groups, clusters, chains of pores, slag and metal inclusions from manual decoding of images is shown.

Algorithms for the search and identification of flaws for use in digital radiography complexes have been developed and experimentally tested at HSC KARS. The convergence of the results of computer and manual decryption was 0,85.

关键词

monitored object, radiographic image, computer decoding, flaw, flaw image, group, cluster, segmentation

全文:

作者简介

S. Grigorchenko

Kolomna Institute (Branch) of the Moscow Polytechnic University

编辑信件的主要联系方式.
Email: rent_sig@mail.ru
俄罗斯联邦, Kolomna

V. Kapustin

TECHNOPROGRESS Research Centre

Email: kapustin@tpcorp.ru
俄罗斯联邦, Moscow

参考

On Industrial Safety of Hazardous Production Facilities: Federal Law No. 116 — FZ dated 07/21/97 : adopted by the State Duma on June 20, 1997 [website]. 1997. Available at: http://www.kremlin.ru/acts/bank/11232 (Accessed on 08.10.2024).
Order of the Federal Service for Environmental, Technological and Nuclear Supervision dated October 20, 2020. No. 420 “On approval of Federal norms and rules in the field of industrial safety “Rules for industrial safety expertise” (with amendments and additions) [Electronic resource] // Information and legal portal “Garant.ru”. Available at: https://base.garant.ru/75039846/#friends (Accessed on 08.10.2024).
X-Vizor — SOFTWARE for digital and computer radiography / Limited Liability Company “Newcom-NDT” [website]. 2024. Available at: https://newcom-ndt.ru/x-vizor (Accessed on 08.10.2024).
The system of image improvement and archiving “SOVA+” / Association of Scientific and technical cooperation “TESTRON” [website]. 2024. Available at: http://www.testron.ru/ru/view/38 (Accessed on 08.10.2024).
Disoft software // Limited Liability Company “Center Tsifra” [website]. 2024. Available at: https://digital-xray.ru/product/po-disoft/#desc (Accessed on 08.10.2024).
Grudsky A. Ya., Velichko V. Ya. Digitization of radiographic images is not very easy // In the world of NDT. 2011. No. 4 (54). P. 74—76.
Grudsky A. Ya., Velichko V. Ya., Dech A. V. How to guarantee the reliability and quality of the digital archive of X-ray images of annular welded joints of the main pipeline? // In the world of NDT. 2012. No. 4 (58). P. 34—40.
Bagaev K. A., Kozlovsky S. S. Digitization of X-ray films. What should be taken into account when developing and implementing Russian standards // In the world of NDT. 2013. No. 3 (61). P. 30—35.
Dech A. V. Requirements for software applications for improving and archiving X-ray images // In the world of NDT. 2003. No. 3 (21). P. 66—68.
Kosarina1 E.I., Krupnina1 O.A., Demidov A.A., Mikhaylova N.A. Digital optical pattern and its dependence on the radiation image at non-destructive testing by method of digital radiography // Aviation Materials and Technologies. 2019. No. 1 (54). P. 37—42. doi: 10.18577/2071-9140-2019-0-1-37-42
Nazarenko S.Yu., Udod V.A. Application of artificial neural networks in radiation non-destructive testing // Defectockopiya. 2019. No. 6. P. 53—64.
Vorobeychikov S.E., Fokin V.A., Udod V.A., Temnik A.K. Estimating the efficiency of two algorithms for segmentation of digital radiation images of test objects // Defectoskopiya. 2017. No. 2. P. 60—67.
Liu T., Zheng P., Bao J., Chen H. A state-of-the-art survey of welding radiographic image analysis: Challenges, technologies and applications // Measurement. 2023. V. 214. P. 112821. doi: 10.1016/j.measurement.2023.112821
Block S. B., Da Silva R. D., Lazzaretti A. E., Minetto R. LoHi-WELD: A Novel Industrial Dataset for Weld Defect Detection and Classification, a Deep Learning Study, and Future Perspectives // IEEE Access. 2024. V. 12. P. 77442—77453. doi: 10.1109/ACCESS.2024.3407019
Say D., Zidi, S., Qaisar S.M., Krichen M. Automated Categorization of Multiclass Welding Defects Using the X-ray Image Augmentation and Convolutional Neural Network // Sensors. 2023. V. 23. P. 6422. https://doi.org/10.3390/s23146422
Zhao S., Long L., An D., Wang Y, Zhang H., Liang H., Jin S. Design and Realization of Nondestructive Testing Information Management System for Shell Electron Beam Welds // Software Engineering and Applications. 2022. V. 11. No. 5. P. 1005—1016. doi: 10.12677/SEA.2022.115103. https://doi.org/10.12677/sea.2022.115103
Harrouche S., Nacereddine N., Goumeidane A.B. A Comparative Study of Different CNN Models using Transfer Learning for Weld Defect Classification in Radiographic Testing // Proc. of the 4th International Conference on Electrical, Communication and Computer Engineering (ICECCE) 30—31 December 2023, Dubai, UAE. doi: 10.1109/ICECCE61019.2023.10442057
Grigorchenko S.A., Efimenko L.A. Automation of computer interpretation of radiation images of welded joints // Defectoskopiya. 2015. No. 1. P. 21—27.
Grigorchenko S.A., Kapustin V.I. Classification of flaws in automated radiographic inspection of welded joints // Defectoskopiya. 2009. V. 45. No. 9. P. 73—87.
Kapustin V.I., Zuev V.M., Ivanov V.I., Dub A.V. Radiographic inspection. Information aspects. Moscow: LLC Publishing House “NAUCHTEKHLITIZDAT”, 2010. 368 p.
NP-105-18. Rules for metal control of equipment and pipelines of nuclear power plants during manufacture and installation.
GOST 23055—78. Non-destructive testing. Fusion welding of metals. Welds classification by radiography testing results
STO Gazprom 2-2.4-917-2014. Instructions for radiographic quality control of welded joints during the construction and repair of field and main pipelines.
Grigorchenko S.A. Automated assessment of the quality of welded joints according to the parameters of radiographic images of flaws // Control. Diagnostics. 2009. No. 10. P. 30—36.