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Tytuł artykułu

Effect of cooling rate and aluminum contents on the Mg-Al-Zn alloys’ structure and mechanical properties

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: This work present an influence of Al concentration and cooling rate on structure and mechanical properties of magnesium alloys. Also the paper presents a methodology to predict crystallization temperatures obtained during crystallization process using an UMSA platform, based on cooling rate and chemical composition and mechanical properties and grain size based on characteristics temperatures. Design/methodology/approach: The experimental magnesium alloy used for thermal analysis and training of neural network was prepared in cooperation with the Faculty of Metallurgy and Materials Engineering of the Technical University of Ostrava and the CKD Motory plant, Hradec Kralove in the Czech Republic. The alloy was cooled with three different cooling rates in UMSA Technology Platform. The following results concern scanning electron microscopy investigations in the SE observation mode, as well as using BSE modus for better phase contrast results, also quantitative microanalysis was applied for chemical composition investigations of the phases occurred. Compression test were conducted at room temperature using a Zwick universal testing machine. Compression specimens were tested corresponding to each of three cooling rates. Rockwell F-scale hardness tests were carried out using a Zwick HR hardness testing machine. Findings: The research show that the thermal analysis carried out on UMSA Technology Platform is an efficient tool for collect and calculate thermal parameters. The formation temperatures of various thermal parameters, mechanical properties (hardness and ultimate compressive strength) and grain size are shifting with an increasing cooling rate. Practical implications: The parameters described can be applied in metal casting industry for selecting magnesium ingot preheating temperature for semi solid processing to achieve requirements properties. The presented models can be applied in computer systems of Mg-Al-Zn casting alloys, selection and designing for Mg-Al-Zn casting parts. Originality/value: The paper contributes to better understanding and recognition an influence of different solidification condition on non-equilibrium thermal parameters of magnesium alloys.
Rocznik
Strony
613--633
Opis fizyczny
Bibliogr. 37 poz., rys., tab., wykr.
Twórcy
  • Division of Material Processing Technology, Management and Computer Techniquesin Materials Science, Institute of Engineering Materials and Biomaterials, Silesian University of Technology, ul. Konarskiego 18a, 44-100 Gliwice, Poland
autor
  • Division of Material Processing Technology, Management and Computer Techniquesin Materials Science, Institute of Engineering Materials and Biomaterials, Silesian University of Technology, ul. Konarskiego 18a, 44-100 Gliwice, Poland
autor
  • Division of Material Processing Technology, Management and Computer Techniquesin Materials Science, Institute of Engineering Materials and Biomaterials, Silesian University of Technology, ul. Konarskiego 18a, 44-100 Gliwice, Poland
Bibliografia
  • [1] P.J. Haines, Principles of thermal analysis and calorimetry, The Royal Society of Chemistry, Cambridge, UK, 2002.
  • [2] M.E. Brown, Introduction to thermal analysis. Techniques and application, Kluwer Academic Publisher, Netherlands, 2001.
  • [3] R.F. Speyer, Thermal analysis of materials, Marcel Dekker, 1994.
  • [4] P. Gabbott, Principles and applications of thermal analysis, Blackwell Publishing, UK, 2008.
  • [5] G.W.H. Hohne, W.F. Hemminger, H.J. Flammersheim, Differential Scanning Calorimetry, Springer-Verlag Berlin Heidelberg, 2003.
  • [6] P. Bassani, E. Gariboldi, A. Tuissi, Calorimetric analysis of AM60 magnesium alloy, Journal of Thermal Analysis and Calorimetry 80 (2005) 739-747.
  • [7] A. Saccone, D. Macciò, S. Delfino, F. H. Hayes, R. Ferro, Mg-Ce alloys, Experimental investigation by Smith thermal analysis, Journal of Thermal Analysis and Calorimetry 66 (2001) 47-57.
  • [8] B. Bronfin, N. Moscovitch, New magnesium alloys for transmission parts, Metal Science and Heat Treatment 48/11-12 (2006) 479-486.
  • [9] D. Eliezer, E. Aghion, F.H. Froes, Magnesium science and technology, Adv Mat Performance 5 (1998) 201-212.
  • [10] ASM Specialty Handbook-Magnesium and Magnesium Alloys, ed. M.M. Avedesian and H. Baker, ASM International, USA, 1999, 3-84.
  • [11] L.A. Dobrzański, T. Tański, L. Čížek, Influence of Al addition on structure of magnesium casting alloys, Journal of Achievements in Materials and Manufacturing Engineering 17 (2006) 221-224.
  • [12] I.J. Polmear, Light Alloys, London, 1995.
  • [13] K.U. Kainer, Magnesium - alloys and technologies, Wiley-VCH Verlag GmbH & Co. KG aA, Weinheim 2003, 33-341.
  • [14] L. Backuerud, G. Chai, J Tamminen, Solidification characteristics of aluminum alloys Vol.2 Foundry Alloys, AFS Skanaluminium, Stockholm, Sweden, 1990
  • [15] L. Backuerud, G. Chai, Solidification characteristics of aluminum alloys Vol. 3 Foundry Alloys, AFS Skanaluminium, Stockholm, Sweden, 1990.
  • [16] L.A. Dobrzański, R. Maniara, J. Sokołowski, W. Kasprzak , Effect of cooling rate on the solidification behavior of AC AlSi7Cu2 alloy, Journal of Materials Processing Technology 191 (2007) 317-320.
  • [17] L.A. Dobrzański, W. Borek, R. Maniara, Influence of the crystallization condition on Al-Si-Cu casting alloys structure, Journal of Achievements in Materials and Manufacturing Engineering 18 (2006) 211-214.
  • [18] S. Jura, Z. Jura, Theory ATD method in studies of Al alloys, Solidification of Metals and Alloys 28 (1996) 57-87 (in Polish).
  • [19] S. Jura, J. Sakwa, Application of thermal-derivative analysis to evaluate the mechanical properties of cast iron, Solidification of Metals and Alloys 5 (1982) 6-29 (in Polish).
  • [20] M. Malekan, S.G. Shabestari, Computer-aided cooling curve thermal analysis used to predict the quality of aluminum alloys, Journal of Thermal Analysis DOI 10.1007/s10973-010-1023-2.
  • [21] M. Kondracki, J. Gawroński, J. Szajnar, R. Grzelczak, K. Podsiadło, The study of the crystallization process MO95 brass based on ATD method, Archives of Foundry 2/4 (2002) 128-134 (in Polish).
  • [22] J. Gawroński, Crystallization of alloys, Thermal and derivation method (ATD), Archives of Foundry 16 (2005) 256-261 (in Polish).
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  • [24] J. Adamiec, A. Kiełbus, J. Cwajna, The procedure of quantitative description of the structure of cast magnesium alloys, Archives of Foundry 6/18 (2006) 209-214 (in Polish).
  • [25] L.A. Dobrzański, T. Tański, Solid State Phenomena, Influence of aluminium content on behaviour of magnesium cast alloys in bentonite sand mould, Journal of Materials Processing Technology 147-149 (2009) 764-769.
  • [26] L.A. Dobrzański, T. Tański, L. Cížek, Heat treatment impact on the structure of die-cast magnesium alloys, Journal of Achievements in Materials and Manufacturing Engineering 20 (2007) 431-434.
  • [27] H. Baker, ASM Specialty Handbook. Magnesium and Magnesium Alloys, Avedesian (Ed.), ASM International, ISBN: 0871706571, (1999) USA.
  • [28] http://uwindsor.ca/umsa
  • [29] “Method and Apparatus for Universal Metallurgical Simulation and Analysis” - United States Patent, Patent No.: US 7,354,491 B2, Date of Patent: Apr. 8, 2008.
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  • [31] 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.
  • [32] L.A. Dobrzanski, T. Tanski, J. Trzaska, L. Čížek, Modelling of hardness prediction of magnesium alloys using artificial neural networks applications, Journal of Achievements in Materials and Manufacturing Engineering 26/2 (2008) 187-190.
  • [33] 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.
  • [34] T. Masters, Neural networks in practice, PWN, Warsaw, 1996 (in Polish).
  • [35] L.A. Dobrzański, S. Malara, J. Trzaska, Project of neural network for steel grade selection with the assumed CCT diagram, Journal of Achievements in Materials and Manufacturing Engineering 27/2 (2008) 155-158.
  • [36] W. Sitek, J. Trzaska, L.A. Dobrzanski, Evaluation of chemical composition effect on materials properties using AI methods, Journal of Achievements in Materials and Manufacturing Engineering 20 (2007) 379-382.
  • [37] J. Trzaska, L.A Dobrzański, A. Jagiełło, Computer programme 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-684c6a4d-9033-49cf-88b1-19adf2e85b30
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