350 rub
Journal Radioengineering №6 for 2018 г.
Article in number:
Allocation of perlite grains on metallographic images of low-carbon steel
Type of article: scientific article
UDC: 004.932.4
Keywords: metallographic image steel microstructure parameters perlite grains of perlites image segmentation morphological processing convex shell boundary detection.
Authors:

R.G. Magdeev – Leading Engineer, «Telekom.ru» LLC (Ulyanovsk)

E-mail: radiktkd2@yandex.ru

A.G. Tashlinsky – Dr.Sc.(Eng.), Professor, Head of Department «Radio Engineering», Ulyanovsk State Technical University E-mail: tag@ulstu.ru

Abstract:

An approach to the automated allocation of areas corresponding to perlite grains on metallographic images is described, along which microstructural parameters of low-carbon steel are determined. The proposed approach includes the procedures of segmentation in the image of the perlite grain areas, their morphological processing to eliminate internal discontinuities and the exclusion of small clusters, the separation of individual grains, the determination of their boundaries, and the construction of convex hulls. Examples of the results of processing procedures obtained on real metallographic images of low-carbon steel 17GS are given.

Pages: 33-37
References
  1. Arzamasova B.N., Muxina G.G. Materialovedenie. M.: Izd-vo MGTU im. N.E’. Baumana. 2008. 648 s.
  2. Starodubov D.N. Metody’ i algoritmy’ obrabotki i analiza defektoskopicheskix i metallograficheskix snimkov: dis. … kand. texn. nauk. Vladimir. 2008. 183 s.
  3. Bhadeshia H., Honeycombe R. Steels: Microstructure and Properties: Fourth Edition. Butterworth-Heinemann. 2017. 488 p.
  4. Lisin Yu.V., Neganov D.A., Surikov V.I., Gumerov K.M. Issledovaniya izmenenij svojstv metalla truboprovodov v proczesse e’kspluataczii: obobshhenie rezul’tatov i perspektivny’e razrabotki Ufimskoj nauchnoj shkoly’ // Nauka i texnologii truboprovodnogo transporta nefti i nefteproduktov. 2017. T. 2. № 2. S. 22−30.
  5. Greben’kov S.K., Skudnov V.A., Shaczov A.A. Deformacziya i razrushenie nizkouglerodisty’x martensitny’x stalej // Metallovedenie i termicheskaya obrabotka metallov. 2016. № 2. S. 33−38.
  6. Gao X., Xie Y., Chen Z., You D. Fractal feature detection of high-strength steel weld defects by magneto-optical imaging // HANJIE XUEBAO. 2017. T. 38. № 7. P. 1−4.
  7. Dement’ev V.E., Magdeev R.G. Primenenie psevdogradientnoj adaptaczii dlya identifikaczii ob’‘ektov na binarny’x izobrazheniyax // Radioe’lektronny’e sistemy’ i ustrojstva dlya infokommunikaczionny’x texnologij. 2013. S. 309−313.
  8. Gonsales R., Vuds R. Czifrovaya obrabotka izobrazhenij. Izd. 3-e, ispravl. i dop. M.: Texnosfera. 2012. 1104 s.
  9. Palamar’ I.N., Sizov P.V. Metod segmentaczii izobrazhenij s primeneniem vy’rashhivaniya oblastej i mnogomasshtabnogo analiza // Vestnik SibGAU im. akad. M.F. Reshetneva. 2010. № 3(29). S. 15−20.
  10. Vizil’ter Yu.V., Zheltov S.Yu., Bondarenko A.V., Osokov M.V., Morzhin A.V. Obrabotka i analiz izobrazhenij v zadachax mashinnogo zreniya. M.: Fizmatkniga. 2010. 672 s.
  11. Andreev A.Yu., Bobkov S.P. Segmentacziya simvolov v izobrazhenii modificzirovanny’m metodom zhuka // Sovremenny’e naukoemkie texnologii. Regional’noe prilozhenie. 2014. № 1 (37). S. 85−88.
  12. Magdeev R.G., Tashlinskij A.G. E’ffektivnost’ identifikaczii ob’‘ektov na binarny’x izobrazheniyax s ispol’zovaniem proczedur psevdogradientnoj adaptaczii // Radiotexnika. 2014. № 7. S. 96−102.
  13. Kormen T., Lejzerson Ch., Rivest R., Shtajn K. Algoritmy’. Postroenie i analiz. Izd. 3-e. M.: Vil’yams. 2013. 1324 s.
  14. Magdeev R.G., Tashlinskij A.G. Sravnitel’ny’j analiz metodov vy’pukloj obolochki na izobrazheniyax mikrostruktur metallov // Mezhvuz. sb. nauch. trudov «Radioe’lektronnaya texnika». Ul’yanovsk: UlGTU. 2015. № 1(7). S. 114−120.
  15. Barber C.B., Dobkin D.P., Huhdanpaa H. The quickhull algorithm for convex hulls // ACM Transactions on Mathematical Software. 1996. V. 22(4). P. 469−483.
Date of receipt: 24 мая 2018 г.