There are a number of tasks that require assessment of the condition of the material and its mechanical characteristics. Such tasks may arise at the production stage, when there’s a need to control the content of various components of the material, strength, hardness, etc. Also similar tasks arise during exploitation of materials, which is especially relevant today, when most of the responsible products and structures in the field of nuclear energy, chemical industry, machine-building industry are on the verge of wearing down. Previously defectoscopy methods were mainly used to assess the reliability of such materials and products. These methods provided information on the presence or absence of a defect. But to prevent accidents, information about the pre-defective state of the material itself and the degree of its degradation is needed. Approaches involving methods and means of solid state physics, mechanics, chemistry, materials science and other scientific disciplines have become more informative for describing the state of degradation. However, these methods are quite laboursome and time consuming and cannot be applied to transient processes. Therefore, it is important to develop a method that would be based on the analysis of the microstructure of the material would allow to obtain its numerical mechanical characteristics. This approach would be used at the production stage of materials to determine their components and mechanical characteristics and at the stage of exploitation to determine the degree of degradation of the material. It is known that the fractal dimension of each microstructure of the material is an indicator of its qualitative characteristics. Thus, the numerical value of the fractal dimension establishes the relationship between the structure and the mechanical properties of the material. In this work the method of localization of fractures of heat-resistant steels on the basis of fractal models of metallographic images is developed and its advantages in comparison with other known approaches are analyzed.
[1] I. M. Zhuravel, “Avtomatychne rozpiznavannia krykhkoho ta viazkoho zlamiv stali 15Kh1MF z vykorystanniam fraktalnoi rozmirnosti” [“Automatic recognition of brittle and viscous fractures of 15Kh1MF steel using fractal dimension”], in Proc. of Scientific and Technical Conf. Computational Methods and Information Transformation Systems, Lviv, Ukraine, Oktober 7–8, 2010, pp. 176–178. [in Ukrainian].
[2] I. M. Zhuravel’, L. M. Svirs’ka, O. Z. Student, R. A. Vorobel’, and H. M. Nykyforchyn, “Automated determination of grain geometry in an exploited steam-pipeline steel”, Materials Science, vol. 45, no. 3, pp. 350–357, 2009. https://doi.org/10.1007/s11003-009-9187-2
[3] S. A. Saltyikov, Stereometricheskaya metallografiya [Stereometric metallography]. Moscow, Russia: Metalurgiya Publ., 1976. [in Russian].
[4] V. V. Panasiuk, Y. M. Nykyforchyn, O. Z. Student, and Z. V. Slobodian, “Zastosuvannia pidkhodiv mekhaniky ruinuvannia do otsinky vodnevoi dehradatsii stalei nafto- ta paroprovodiv” [“Application of destruction mechanics approaches to the assessment of hydrogen degradation of steels of oil and steam pipelines”], in Mekhanika i fizyka ruinuvannia budivelnykh materialiv ta konstruktsii [Mechanics and physics of destruction of building materials and structures]. Lviv, Ukraine: Kameniar Publ., 2002. pp. 537–546. [in Ukrainian].
[5] M. V. Karuskevych, et al. “Strukturna poshkodzhuvanist i ruinuvannia zrazkiv-svidkiv vtomnoho poshkodzhennia” [“Structural damage and destruction of witness samples of fatigue damage”], Avyatsyonno-kosmycheskaia tekhnyka i tekhnolohyia [Aerospace engineering and technology], vol. 9 (56), pp. 110–114, 2008. [in Ukrainian].
[6] R. M. Haralik, “Statisticheskiye i strukturnyiye podhody k opisaniyu tekstur” [“Statistical and structural approaches to describing textures”], Trudy instituta inzhenerov po elektrotekhnike i radioelektronike [Proceedings of the Institute of Electrical and Electronics Engineers], vol. 67, no. 5, pp. 98–120, 1979. [in Russian]. https://doi.org/10.1109/PROC.1979.11328
[7] V. I. Bolshakov, V. N. Volchuk, and Yu. I. Dubrov, Fraktaly v materialovedenii [Fractals in Materials Science]. Kyiv, Ukraine: PGASA Publ., 2005. [in Russian].
[8] V. M. Volchuk, “Rozrobka i doslidzhennia metodu vyznachennia yakisnykh kharakterystyk metalu na osnovi analizu fraktalnoi rozmirnosti yoho mikrostruktury” [“Development and research of a method for determining the qualitative characteristics of a metal based on the analysis of the fractal dimension of its microstructure”], Ph.D. dissertation, Prydniprovska State Academy of Civil Engineering and Architecture, Dnipropetrovsk, Ukraine, 2003. [in Ukrainian].
[9] V. I. Bolshakov, Yu. I. Dubrov, and F. V. Kryulin, V. M. Volchuk, “Sposib vyznachennia fraktalnoi rozmirnosti zobrazhen” [“The method of determining the fractal dimension of images”], UA Patent 51439A, February 02, 2002. [in Ukrainian].
[10] I. M. Zhuravel, “Metod binaryzatsii metalohrafichnykh zobrazhen z optymalnym porohom” “[Method of binarization of metallographic images with optimal threshold”], Shtuchnyi intelekt [Artificial Intelligence], vol. 4, pp. 142–147, 2012. [in Ukrainian].
[11] I. M. Zhuravel, and R. A. Vorobel, “Obchyslennia fraktalnykh rozmirnostei z vykorystanniam poverkhnevoho intehrala” [“Calculation of fractal dimensions using the surface integral”], Vidbir i obrobka informatsii [Information extraction and processing], vol. 26 (102), pp. 95–98, 2007. [in Ukrainian].
[12] I. M. Zhuravel, “Vybir nalashtuvan pid chas obchyslennia polia fraktalnykh rozmirnostei zobrazhennia” [“Select settings when calculating the fractal dimension field of an image”], Naukovyi visnyk NLTU Ukrainy [Scientific Bulletin of UNFU], vol. 2 (28), pp. 159–163, 2018. [in Ukrainian]. https://doi.org/10.15421/40280230
[13] A. A. Rogov, “Primenenie spektra fraktalnyih razmernostey Reni kak invarianta graficheskogo izobrazheniya” [“Application of the spectrum of Renyi fractal dimensions as an invariant of a graphic image”], Vestnik Sankt-Peterburgskogo universiteta [Bulletin of St. Petersburg University], vol. 2 (10), pp. 30–43, 2008. [in Russian].
[14] L. Shapiro, and Dzh. Stokman, Kompyuternoe zrenie [Computer vision]. Moscow, Russia: Binom Publ., 2006. [in Russian].
[15] L. Vincent, and P. Soille, “Watersheds in Digital Space: An Efficient Algorithms based on Immersion Simulation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6 (13), pp.583–598, 1991. https://doi.org/10.1109/34.87344
[16] E. P. Putyatin, and S. I. Averin, Obrabotka izobrazheniy v robototehnike [Image processing in robotics]. Moscow, Russia: Mashinostroenie Publ. 1990. [in Russian].
[17] E. I. Krutasova, Nadezhnost metalla energeticheskogo oborudovaniya [Reliability of metal power equipment], Moscow, Russia: Energoizdat Publ. 1981. [in Russian].
[18] A. P. Tsapaev, and O. V. Kretinin, “Metodyi segmentatsii izobrazheniy v zadachah obnaruzheniya defektov poverhnosti” [“Image segmentation methods for surface defect detection”], Kompyuternaya optika [Computer optics], vol. 3 (36), pp. 448–452, 2012. [in Russian].
[19] E. I. Sergeeva, “Invariantnyie integralnyie harakteristiki multispektralnyih kosmicheskih izobrazheniy” [“Invariant integral characteristics of multispectral space images”], Zbirnyk naukovykh pratsʹ Natsionalʹnoho hirnychoho universytetu [Сollection of Research Papers of the National Mining University], vol. 2, no. 35, pp. 118–122, 2010. [in Russian].
[20] V. K. Ivanov, R. E. Paschenko, and A. M. Stadnik, “Ispolzovanie teorii fraktalov dlya analiza radiolokatsionnyih izobrazheniy poverhnosti Zemli” [“Using the theory of fractals for analysis of radar images of the Earth's surface”], Uspehi sovremennoy radioelektroniki [Advances in modern radioelectronics], vol. 5, pp. 17–45, 2006. [in Russian].
[21] A. A. Potapov, Noveyshie metodyi obrabotki izobrazheniy [Latest image processing techniques]. Moscow, Russia: Fizmatlit Publ., 2008. [in Russian].