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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-article-BWAN-0002-0052

Czasopismo

Journal of Achievements in Materials and Manufacturing Engineering

Tytuł artykułu

Modelling of hardness prediction of magnesium alloys using artificial neural networks applications

Autorzy Dobrzański, L. A.  Tański, T.  Trzaska, J.  Čížek, L. 
Treść / Zawartość http://www.journalamme.org
Warianty tytułu
Języki publikacji EN
Abstrakty
EN Purpose: In the following paper there have been presented the optimisation of heat treatment condition and structure of the MCMgAl12Zn1, MCMgAl9Zn1, MCMgAl6Zn1, MCMgAl3Zn1 magnesium cast alloy as-cast state and after a heat treatment. Design/methodology/approach: Working out of a neural network model for simulation of influence of temperature, solution heat treatment and ageing time and aluminium content on hardness of the analyzed magnesium cast alloys. Findings: The different heat treatment kinds employed contributed to the improvement of mechanical properties of the alloy with the slight reduction of its plastic properties. Research limitations/implications: According to the alloys characteristic, the applied cooling rate and alloy additions seems to be a good compromise for mechanical properties and microstructures, nevertheless further tests should be carried out in order to examine different cooling rates and parameters of solution treatment process and aging process. Practical implications: For comparison of the achieved results on the basis of the performed investigations a computer neural network model was used for analysis of the aluminium content and heat treatment parameters influence on the properties of the worked out cast magnesium alloys. Originality/value: The advantage of the neural networks is their capability to learn and adapt to the changing condition, as well as their capability to generalise the acquired knowledge.
Słowa kluczowe
PL obróbka cieplna   właściwości mechaniczne   sztuczne sieci neuronowe   stopy magnezu  
EN heat treatment   mechanical properties   artificial neural networks   magnesium alloys  
Wydawca International OCSCO World Press
Czasopismo Journal of Achievements in Materials and Manufacturing Engineering
Rocznik 2008
Tom Vol. 26, nr 2
Strony 187--190
Opis fizyczny Bibliogr. 15 poz., tab., wykr.
Twórcy
autor Dobrzański, L. A.
autor Tański, T.
autor Trzaska, J.
autor Čížek, L.
  • 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, leszek.dobrzanski@polsl.pl
Bibliografia
[1] A. Necat, K. Rasit, Use of artificial neural network for prediction of physical properties and tensile strengths in particle reinforced alüminum matrix composites, Journal of Materials Science 40 (2005) 1767-1770.
[2] S. Malinov, W. Sha, The neural network modeling of titanium alloy phase transformation and mechanical properties, Jom 57 (2005) 54-57.
[3] V. V. Kurban, N. L. Yatsenko, V. I. Belyakova, Feasibility of using neural networks for real-time prediction of the mechanical properties of finished rolled products, Metallurgist 51 (2007) 3-6.
[4] S. Juan-hua, L. He-jun, D. Qi-ming, L. Ping, K. Bu-xi, Prediction and analysis of the aging properties of rapidly solidified Cu-Cr-Sn-Zn alloy through neural network, Journal of Materials Engineering and Performance, 14 (2005) 363-366.
[5] L. A. Dobrzański, J. Trzaska, Application of neural networks for prediction of critical values of temperatures and time of the supercooled austenite transformations, Journal of Materials Processing Technology 155-156 (2004) 1950-1955.
[6] M. Krupiński, L. A. Dobrzański, J. Sokołowski, W. Kasprzak, G. Byczyński, Methodology for automatic control of automotive Al-Si cast components, Materials Science Forum 539-543 (2007) 339-344.
[7] J. Trzaska, L. A. Dobrzański, 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 164-165 (2005) 1637-1643.
[8] L. A. Dobrzański, T. Tański, L. Čížek, Z. Brytan, Structure and properties of the magnesium casting alloys, Journal of Materials Processing Technology 192-193 (2007) 567-574.
[9] A. Fajkiel, P. Dudek, G. Sęk-Sas, Foundry engineering XXI c. Directions of metallurgy development and Light alloys casting, Publishers Institute of Foundry engineering, Cracow, 2002 (in Polish).
[10] M. Greger, R. Kocich, L. Čížek, L. A. Dobrzański, I. Juřička, Possibilities of mechanical properties and microstructure improvement of magnesium alloys, Archives of Materials Science and Engineering 28/2 (2007) 83-90.
[11] K. U. Kainer, Magnesium-Alloys and Technology, Wiley-VH, Weinheim, Germany, 2003.
[12] A. Kiełbus, Structure and mechanical properties of casting MSR-B magnesium alloy, Journal of Achievements in Materials and Manufacturing Engineering 18 (2006) 131-134.
[13] T. Rzychoń, A. Kiełbus, Microstructure of WE43 casting magnesium alloys, Journal of Achievements in Materials and Manufacturing Engineering 21 (2007) 31-34.
[14] T. Rzychoń, A. Kiełbus, The influence of wall thickness in the microstructure of HPDC AE44 alloys, Archives of Materials Science and Engineering 28/8 (2007) 471-474.
[15] M. Yong, A. Clegg, Process optimization for a squeeze cast magnesium alloy, Journal of Materials Processing Technology 145 (2004) 134-141.
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