PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Determination of the best microstructure and titanium alloy powders properties using neural network

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: Create a software product using a probabilistic neural network (PNN) and database based on experimental research of titanium alloys to definition of the best microstructure and properties of aerospace components. Design/methodology/approach: The database creation process for artificial neural network training was preceded by the investigation of the granulometric composition of the titanium powder alloys, study of microstructure, phase composition and evaluation of micromechanical properties of these alloys by the method of material plasticity estimation in the conditions of hard pyramidal indenters application. A granulometric analysis was done using a special complex of materials science for the images analysis ImageJ. Metallographic investigations of the powder structure morphology were carried out on the scanning electron microscope EVO 40XVP. Specimens for micromechanical testing were obtained by overlaying the titanium alloy powders on the substrate made of the material close to chemical composition. Substrates were prepared by pressing the powder mixture under the load of 400 MPa and following sintering at 1300°C for 1 hour. Overlaying was performed by an electron gun ELA-6 (beam current – 16 mA). Findings: According to the modelling results, a threshold of the PNN accuracy was established to be over 80%. A high level of experimental data reproduction allows us a full or partial replacement of a number of experimental investigations by neural network modelling, noticeably decreasing, in this case, the cost of the material creation possessing the preset properties with preserved quality. It is expected that this software can be used for solving other problems in materials science too. Research limitations/implications: The accuracy of the PNN algorithm depends on the number of input parameters obtained experimentally and forms a database for the training of the system. For our case, the amount of input data is limited. Practical implications: Previously trained system based on the PNN algorithm will reduce the number of experiments that significantly reduce costs and time to study. Originality/value: A software product on the basis of the PNN network was developed. The training sample was built based on a series of laboratory studies granulometric composition of the titanium powder alloys, study of microstructure, phase composition and evaluation of micromechanical properties of powder materials. The proposed approach allows us to determine the best properties of the investigated material for the design of aerospace components.
Rocznik
Strony
25--31
Opis fizyczny
Bibliogr. 28 poz., rys., tab.
Twórcy
  • Lviv Polytechnic National University, 12 Bandera St., Lviv, 79013, Ukraine
  • The John Paul II Catholic University of Lublin, Al. Racławickie 14, 20-950 Lublin, Poland
  • Lviv Polytechnic National University, 12 Bandera St., Lviv, 79013, Ukraine
  • Lviv Polytechnic National University, 12 Bandera St., Lviv, 79013, Ukraine
  • Lviv Polytechnic National University, 12 Bandera St., Lviv, 79013, Ukraine
  • Lviv Polytechnic National University, 12 Bandera St., Lviv, 79013, Ukraine
autor
  • Lviv Polytechnic National University, 12 Bandera St., Lviv, 79013, Ukraine
  • Lviv Polytechnic National University, 12 Bandera St., Lviv, 79013, Ukraine
Bibliografia
  • [1] Z.A. Duryagina, S.A. Bespalov, A.K. Borysyuk, V.Ya. Pidkova, Magnetometric analysis of surface layers of 12X18H10T steel after ion-beam nitriding, Metallofizika i noveishie tekhnologii 33/5 (2016) 29-51.
  • [2] A. Śliwa, Application of the Finite Elements Method for computer simulation of properties of surface layers, Archives of Materials Science and Engineering 86/2 (2017) 56-85.
  • [3] E. Wołowiec-Korecka, Methods of data mining for modelling of low-pressure heat treatment, Journal of Achievements in Materials and Manufacturing Engineering 85/1 (2017) 31-40.
  • [4] V.G. Efremenko, K. Shimizu, A.P. Cheiliakh, T.V. Pastukhova, Yu.G. Chabak, K. Kusumoto, Abrasive resistance of metastable V-Cr-Mn-Ni spheroidal carbide cast irons using the factorial design method, International journal of minerals, metallurgy, and materials 23/6 (2016) 645-657, doi: https://doi.org/10.1007/s 12613-016-1277-1.
  • [5] K.-L. Du, M.N.S. Swamy, Neural Networks and Statistical Learning, Springer-Verlag, London, 2014.
  • [6] J.S. Armstrong, Illusions in Regression Analysis, International Journal of Forecasting 28/3 (2012) 557-766.
  • [7] Y. Rashkevych, D. Peleshko, O. Vynokurova, I. Izonin, N. Lotoshynska, Single-frame image super-resolution based on singular square matrix operator Proceedings of the 1st Ukraine Conference "Electrical and Computer Engineering" UKRCON'2017, Kiev, 2017, 944-948, doi: 10.1109/UKRCON.2017.81 00390.
  • [8] A.B. Badiru, J.Y. Cheung, Fuzzy engineering expert systems with neural network applications, John Wiley & Sons, Inc., 2002.
  • [9] Ye. Bodyanskiy, I. Perova, O. Vynokurova, I. Izonin, Adaptive Wavelet Diagnostic Neuro-Fuzzy System for Biomedical Tasks, Proceedings of the 14th International Conference "Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering" TCSET'2018, Lviv-Slavske, 2018, 299-303.
  • [10] R. Tkachenko, P. Tkachenko, I. Izonin, Y. Tsymbal, Learning-based image scaling using neural-like structure of geometric transformation paradigm, in: Advances in Soft Computing and Machine Learning in Image Processing, A.E. Hassanien, D.A. Oliva (Eds.). SCI, Vol. 730, Springer, Cham, 2018, 537-565, doi: https://doi.org/10.1007/978-3-319-63754-925.
  • [11] R.P. Cherian, L.N. Smith, P.S. Midha, A neural network approach for selection of powder metallurgy materials and process parameters, Artificial Intelligence in Engineering 14/1 (2000) 39-44, doi: https://doi.org/10.1016/S0954-1810(99)00026-6.
  • [12] O.P. Ostash, I.M. Andreiko, V.V. Kulyk, l.H. Uzlov, O.I. Babachenko, Fatigue durability of steels of railroad wheels, Materials Science 43/3 (2007) 403-414, doi: https://doi.org/10.1007/s 11003-007-0046-8.
  • [13] O.P. Ostash, I.M. Andreiko, V.V. Kulyk, I.H. Uzlov, K.I. Uzlov, O.I. Babachenko, Low-temperature cyclic crack resistance of steels of railroad wheels, Materials Science 44/4 (2008) 524-529, doi: https://doi.org/10.1007/s11003-009-9112-8.
  • [14] L.E. Kharchenko, O.E. Kunta, O.I. Zvirko, R.S. Savula, Z.A. Duryahina, Diagnostics of hydrogen macrodelamination in the wall of a bent pipe in the system of gas mains. Materials Science 51/4 (2016) 530-537, doi: https://doi.org/10.1007/s11003-016-9872-x.
  • [15] O.P. Ostash, J.M. Andreiko, V.V. Kulyk, V.J. Vavrukh, Influence of braking on the microstructure and mechanical behavior of railroad wheel steels, Materials Science 48/5 (2013) 569-574, doi: https://doi.org/10.1007/s11003-013-9539-9.
  • [16] M.I. Pashechko, V.V. Shyrokov, Z.A. Duryahina, Kh.B. Vasvliv, Structure and corrosion-mechanical properties of the surface layers of steels after laser alloying, Materials Science 39/1 (2003) 108-117, doi: https://doi.org/10.1023/A:1026134714719.
  • [17] O.P. Ostash, V.V. Kulyk, V.D. Poznyakov, O.A.Haivorons'kyi, L.I. Markashova, V.V. Vira, Z.A.Duriagina, T.L. Tepla, Fatigue crack growth resistance of welded joints simulating the weld-repaired railway wheels metal, Archives of Materials Science and Engineering 86/2 (2017) 49-55, doi:10.5604/01.3001.0010.4885.
  • [18] M. Qian, F.H. Froes. Titanium Powder Metallurgy. Butterworth-Heinemann, USA, 2015.
  • [19] G. Chen, S.- Y. Zhao, P. Tan, J.-O. Yin, Q. Zhou, Y. Ge, Z.-F. Li, J. Wang, H.-P. Tang, P. Cao, Shape memory TiNi powders produced by plasma rotating electrode process for additive manufacturing, Transactions of Nonferrous Metals Society of China 27 (2017) 2647-2655, doi: https://doi.org/10.1016/S1003-6326(17)60293-0.
  • [20] F. Cao, K.S. Ravi Chandran, P. Kumar, New approach to achieve high strength powder metallurgy Ti-6Al-4V alloy through accelerated sintering at β-transus temperature and hydrogenation-dehydrogenation treatment. Scripta Materialia 130 (2017) 22-26, doi: https://doi.org/10.1016/j.scriptamat.2016.11.005.
  • [21] J.-M. Oh, K.-M. Roh, B.-K. Lee, C.-Y. Suh, W. Kim, H. Kwon, J.-W. Lim, Preparation of low oxygen content alloy powder from Ti binary alloy scrap by hydrogenation-dehydrogenation and deoxidation process, Journal of Alloys and Compounds 593 (2014) 61-66, doi: https://doi.org/10.1016/j.jallcom.2014.01.033.
  • [22] Y. Zhang, Ch. Wang, Y. Liu, Sh. Liu, S. Xiao, Y. Chen, Surface characterizations of TiH2 powders before and after dehydrogenation, Applied Surface Science 410 (2017) 177-185, doi: https://doi.org/10.1016/j.apsusc.2017.03.077.
  • [23] Z.A. Duriagina, A.M. Trostianchyn, I.A. Lemishka, A.A. Skrebtsov, O.V. Ovchinnikov, Granulometric charactetistcs of Ti-Al-V-Mo-Zr titanium alloy obtained by the method of centrifugal plasma dispersion of electrode, Metal Science and Treatment of Metals 1 (2017) 45-51 (in Ukrainian).
  • [24] Z.A. Duriagina, A.M. Trostianchyn, l.A. Lemishka, A.A. Skrebtsov, O.V. Ovchinnikov, The influence of chemical-thermal treatment on granulometric characteristics of titanium sponge powder, Ukrainian Journal of Mechanical Engineering and Materials Science 3/1 (2017) 73-80, URI: http://ena.lp.edu.ua:8080/handle/ntb/39554.
  • [25] Yu. Milman, S. Chugunova, I. Goncharova, Plasticity at absolute zero as a fundamental characteristic of dislocation properties, International Journal of Materials Science and Applications 3/6 (2014) 353-362, doi: 10.11648/j.ijmsa.20140306.22.
  • [26] Z.A. Duryahina, T.M. Kovbasyuk, S.A. Bespalov, V.Y. Pidkova, Micromechanical and electrophysical properties of Al2O3 nanostructured dielectric coatings on plane heating elements. Materials Science 52/1 (2016) 50-55 doi: https://doi.org/10.1007/s11003-016-9925-1.
  • [27] J. Broeke, J. Maria, M. Perez, J. Pascau, Image processing with lmageJ, 2nd Edition, Packt Publishing, 2015.
  • [28] T. Rashid, Make your own neural network: Createspace Independent Publishing Platform, 2016.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c52e2881-2542-407f-bbcb-814f65ebc15a
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.