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Numerical simulation of the alloying elements effect on steels’ properties

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Wybrane pełne teksty z tego czasopisma
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Języki publikacji
EN
Abstrakty
EN
Purpose: The goal of the research carried out was evaluation of alloying elements effect on high-speed steels hardness and fracture toughness and austenite transformations during continuous cooling of structural steels. Design/methodology/approach: Multi-layer feedforward neural networks with learning rule based on the error backpropagation algorithm were employed for modelling the steels properties. Then the neural networks worked out were employed for the computer simulation of the effect of particular alloying elements on the steels’ properties. Findings: Obtained results show that neural network are useful in evaluation of synergic effect of alloying elements on selected materials properties when classical investigations’ results do not provide evaluation of the effect of two or more alloying elements. Practical implications: Numerical simulation presented in the work, based on using the adequate material models may feature an alternative for classical investigations on effect of alloying elements on steels’ properties. Originality/value: The use of the neural networks as an tool for evaluation of the chemical composition effect on steels’ properties.
Rocznik
Strony
71--78
Opis fizyczny
Bibliogr. 24 poz., rys., tab.
Twórcy
autor
  • Division of Materials Processing Technology, Management and Computer Techniques in Materials Science, Institute of Engineering Materials and Biomaterials, Silesian University of Technology, ul. Konarskiego 18a, 44-100 Gliwice, Poland
autor
  • Division of Materials Processing Technology, Management and Computer Techniques in Materials Science, Institute of Engineering Materials and Biomaterials, Silesian University of Technology, ul. Konarskiego 18a, 44-100 Gliwice, Poland
Bibliografia
  • [1] A. Śliwa, L.A. Dobrzański, W. Kwaśny, M. Staszuk, Simulation of the microhardness and internal stresses measurement of PVD coatings by use of FEM, Journal of Achievements in Materials and Manufacturing Engineering 43/2 (2010) 684-691.
  • [2] W. Kwaśny, J. Mikuła, L.A. Dobrzański, Fractal and multifractal characteristics of coatings deposited on pure oxide ceramics, Journal of Achievements in Materials and Manufacturing Engineering 17 (2006) 257-260.
  • [3] M.J. Jackson, Numerical analysis of small recessed silicon carbide grinding wheels, Journal of Achievements in Materials and Manufacturing Engineering 43/1 (2010) 27-37.
  • [4] W. Kapturkiewicz, A.A. Burbelko, E. Fraś, M. Górny, D. Gurgul, Computer modelling of ductile iron solidification using FDM and CA methods, Journal of Achievements in Materials and Manufacturing Engineering 43/1 (2010) 310-323.
  • [5] P. Malinowski, J.S. Suchy, Database for foundry engineers - simulationDB - a modern database storing simulation results, Journal of Achievements in Materials and Manufacturing Engineering 43/1 (2010) 349-352.
  • [6] A. Śliwa, J. Mikuła, L.A. Dobrzański, FEM application for modelling of PVD coatings properties, Journal of Achievements in Materials and Manufacturing Engineering 41 (2010) 164-171.
  • [7] E. Mares, J.H. Sokolowski, Artificial intelligence-based control system for the analysis of metal casting properties, Journal of Achievements in Materials and Manufacturing Engineering 40/2 (2010) 149-154.
  • [8] J. Madejski, Dynamic scheduling for agent based manufacturing systems, Journal of Achievements in Materials, and Manufacturing Engineering 40/1 (2010) 66-69.
  • [9] G. Wróbel, J. Kaczmarczyk, Numerical simulation of fatigue degradation process of polymer materials using diagnostic acoustic characteristics, Journal of Achievements in Materials and Manufacturing Engineering 36/2 (2009) 168-175.
  • [10] B. Smoljan, D. Iljkić, S. Smokvina Hanza, Computer simulation of working stress of heat treated steel specimen, Journal of Achievements in Materials and Manufacturing Engineering 34/2 (2009) 152-156.
  • [11] D. Słota, Calculation of the Cooling Condition in the Phase Change Problem, Journal of Achievements in Materials and Manufacturing Engineering 33/1 (2009) 70-77.
  • [12] L.A. Dobrzański, R. Honysz, Artificial intelligence and virtual environment application for materials design methodology, Archives of Materials Science and Engineering 45/2 (2010) 69-94.
  • [13] S. Delijaicov, A.T. Fleury, F.P.R. Martins, Application of multiple regression and neural networks to synthesize a model for peen forming process planning, Journal of Achievements in Materials and Manufacturing Engineering 43/2 (2010) 651-656.
  • [14] F. Musharavati, A.S.M. Hamouda, Application of artificial neural networks for modelling correlations in age, hardenable aluminium alloys, Journal of Achievements in Materials and Manufacturing Engineering 41 (2010) 140-146.
  • [15] L.A. Dobrzański, R. Honysz, Application of artificial neural networks in modelling of quenched and tempered structural steels mechanical properties, Journal of Achievements in Materials and Manufacturing Engineering 40/1 (2010) 50-57.
  • [16] L.A. Dobrzański, M. Król, Application of the neural network for Mg-Al-Zn mechanical properties modelling, Journal of Achievements in Materials and Manufacturing Engineering 37/2 (2009) 549-555.
  • [17] L.A. Dobrzański, A. Zarychta, M. Ligarski, E. Hajduczek, The importance of Nb and Ti as alloying elements in W-Mo-V high-speed steels, Silesian University of Technology Publishing House, Gliwice, 1994 (in Polish).
  • [18] L.A. Dobrzański, A. Zarychta, E. Hajduczek, M. Ligarski, Heat-treatment of W-Mo-V and W-V with addition of Ti high-speed steels, Silesian University of Technology Publishing House, Gliwice, 1997 (in Polish).
  • [19] L.A. Dobrzański: Structure and properties of high-speed steels, Silesian University of Technology Publishing House, Gliwice, 1998.
  • [20] EN ISO 4957:2001, Tool steel.
  • [21] http: //www.erasteel.com/us/produits/hss.php
  • [22] L.A. Dobrzański, J. Trzaska, Application of neural networks for prediction of hardness and volume fractions of structural components constructional steels cooled from the austenitising temperature, Materials Science Forum 437-438 (2003) 359-362.
  • [23] L.A. Dobrzański, J. Trzaska, Application of neural networks to forecasting the CCT diagram, Journal of Materials Processing Technology 157-158 (2004) 107-113.
  • [24] J. Trzaska, L.A. Dobrzański, A. Jagiełło, Computer program for prediction steel parameters after heat treatment, Journal of Achievements in Materials and Manufacturing Engineering 24/2 (2007) 171-174.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0214caf4-09cd-444a-a01d-83b8d69da96e
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