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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-article-BPZ5-0004-0036

Czasopismo

International Journal of Applied Mechanics and Engineering

Tytuł artykułu

Modeling of magnetic properties of bulk amorphous alloys using an artificial neural network tool

Autorzy Szota, M.  Borkowski, S.  Nabiałek, M. 
Treść / Zawartość http://www.ijame.uz.zgora.pl/ http://www.degruyter.com/view/j/ijame
Warianty tytułu
Języki publikacji EN
Abstrakty
EN This paper presents the possibility of using neural networks model for designing magnetic properties of bulk amorphous alloys. In recent years large quantity of data has been published about production methods of bulk metallic glasses (BMG). The most popular methods are: suction casting (Inoue and Tao, 1995; Ma et al., 2005) and injection casting (Park and Kim, 2004). From the microstructure investigation it was found that samples obtained as plates in contrast to rods have full amorphous microstructure. Samples as rods were partially crystallized. This process is complex and difficult as multi-parameter changes are non-linear. This fact and lack of mathematical algorithms describing this process make modeling properties of bulk amorphous-alloys by traditional numerical methods difficult or even impossible. In this case, it is possible to use an artificial neural network. Using neural networks for modeling is caused by several net features: non-linear character, the ability to generalize the results of calculations different from the learning data set, lack of need of mathematical algorithms describing influence of input parameters changes on modeling materials properties. The neural network structure is designed and specially prepared by choosing input and output parameters of the process. The method of neural network learning and testing, the way of limiting net structure and minimizing learning and testing error are discussed. Such a neural network model, after putting desirable values of bulk amorphous alloys properties in the output layer, can give answers to a lot of questions about production process. The practical implications of the neural network models are the possibility of using them to build control system capable of on-line process control and supporting engineering decision in real time. The originality of this research is a new idea to obtain desirable bulk amorphous alloys properties after crystallization process. The specially prepared neural network model could be a help for engineering decisions made in real time.
Słowa kluczowe
PL modelowanie sieci neuronowych   sztuczna inteligencja   inżynieria materiałowa   stopy amorficzne   właściwości magnetyczne  
EN neural network modeling   artificial intelligence   materials engineering   bulk amorphous alloys   magnetic properties  
Wydawca Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
Czasopismo International Journal of Applied Mechanics and Engineering
Rocznik 2010
Tom Vol. 15, no 2
Strony 571--578
Opis fizyczny Bibliogr. 18 poz., rys., tab., wykr.
Twórcy
autor Szota, M.
autor Borkowski, S.
autor Nabiałek, M.
  • Czestochowa University of Technology Faculty of Materials Processing Technology and Applied Physics Materials Engineering Institute 69 Dabrowskiego Street, 42-200 Czestochowa, POLAND, mszota@mim.pcz.czest.pl
Bibliografia
Dobrzanski L.A., Kowalski M. and Madejski J. (2005): Methodology of the mechanical properties prediction for the metallurgical products from the engineering steels Rusing the Artificial Intelliegence methods. – Journal of Materials Processing Technology, vol.164-165, pp.1500-1509.
Du X.H., Haung J.C., Liu C.T. and Lu Z.P. (2008): New criterion of glass forming ability for bulk metallic glasses. – J. Appl. Phys., vol.101.
Haykin S. (1994): Neural networks, a comprehensive foundation. – Macmillan College Publishing Company, New York.
Inoue A. and Tao Z. (1995): Fabrication of Bulky Zr-Based Glassy Alloys by Suction Casting into Copper Mold Materials Transactions. – JIM - Japan Institute of Metals, vol.36, No.9, pp.1184–1187.
Joon-Sik Son and Duk-Man Lee (2008): A study on on-line learning neural networks for prediction for rolling force in hot-rolling mill. – Journal of Materials Processing Technology, vol.164-165, pp.1327-1335.
Kusiak J., Svietlichnyj D. and Pietrzyk M. (2000): Application of artificial neural network in on-line control pf hot flat folling processes. – Int. Journal Engineering Simulation, vol.1, No.3, pp.17-23.
Ma C., Tai N.H. and Chen L.J. (1994): Effects of Processing Methods and Parameters on the Mechanical Properties and Microstructure of Carbon/Carbon Composites. – Journal of Materials Science, vol.29, pp.5859–5869.
Mitrović M., Roth S., Eckert J., Mickel C. and Mitrović N. (2002): Microstructure evolution and soft magnetic properties of Fe72−xNbxAl5Ga2P11C6B4 (x = 0,2) metallic glasses. – Journal of Physics D: Applied Physics, vol.35, pp.2247.
Ohta M. and Yoshizawa Y. (2008): Magnetic properties of high-Bs Fe–Cu–Si–B nanocrystalline soft magnetic alloys. – Journal of Magnetism and Magnetic Materials, vol.320, No.20, pp.e750–e753.
Olszewski J., Zbroszczyk J., Sobczyk K., Ciurzyńska W., Brągiel P., Nabiałek J., Świerczek M., Hasiak M. and Łuniewska A. (2008): Thermal Stability and Crystallizationof Iron and Cobalt. Based Bulk Amorphous Alloys – Acta Physica Polonica, A, vol.114, No.6, pp.1659–1666.
Osowski S. (2003): Neural Network for informations transformation. – Pub. Warsaw University of Technology, Warsaw.
Park E.S. and Kim D.H.(2004): Formation of Ca–Mg–Zn bulk glassy alloy by casting into cone-shaped copper mold. – Journal of Materials Research, vol.19, pp.685–688.
Rutkowski L. (1996): Neural networks and neuro-computers. – Pub. Czestochowa University of Technology, Czestochowa.
Sitek W. and Dobrzanski L.A.(2005): Application of genetic method in materials’ design. – Journal of Materials Processing Technology, vol.164-165, pp.1607-1611.
Svietlicznyj D. and Pietrzyk M. (2001): On-line Model of Thermal Roll Profile during Hot Rolling, Metall. – Foundry Eng., vol.1, pp.27.
Trzaska J. and Dobrzanski L.A.(2005): Application of neural networks for designing the chemical composition of steel with the assumed hardness after cooling from the austenitising temperature. – Journal of Materials Processing Technology, vol.164-165, pp.1950-1956.
Yoshizawa Y., Kakimoto E. and Doke K. (2007): Soft magnetic properties in bulk nanocrystalline alloys fabricated by a shock-wave sintering. – Material Science and Engineering, A, vol.449–451, pp.480–484.
Zhao Z.J., Zhang J.C., Yang X.L., Seet H.L. and Li X.P. (2007): Structure and magnetic properties of iron/cobalt-based glass-covered microwires. – Physica Scripta, pp.153–156.
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