Application of the neural network for Mg-Al-Zn mechanical properties modelling

• Autorzy: Dobrzański L. A. , Król M.
• Języki publikacji: EN

Abstrakty

EN

Purpose: The paper presents results of the research connected with the development of new approach based on the neural network to predict chemical composition and cooling rate for the mechanical properties of the Mg-Al-Zn cast alloys. The independent variables on the model are chemical composition of Mg-Al-Zn cast alloys and cooling rate. The dependent parameters are hardness, ultimate compressive strength and grain size. Design/methodology/approach: The experimental magnesium alloy used for 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. 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 results of this investigation show that there is a good correlation between experimental and predicted dates and the neural network has a great potential in mechanical behaviour modelling of Mg-Al-Zn alloys. Practical implications: The presented model can be applied in computer system of Mg-Al-Zn casting alloys, selection and designing for Mg-Al-Zn casting parts. Originality/value: The presented model can be applied in computer system of Mg-Al-Zn casting alloys, selection and designing for Mg-Al-Zn casting parts.

Słowa kluczowe

EN

numerical technique; neural network; mechanical properties; magnesium alloy

PL

technika nuneryczna; sieci neuronowe; właściwości mechaniczne; stop magnezowy

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2009
• Tom: Vol. 37, nr 2
• Strony: 549--555
• Opis fizyczny: Bibliogr. 16 poz., rys., tabl.

Twórcy

autor

Dobrzański L. A.

autor

Król M.

• 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

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• [3] J. Sungmoon, G. Jamshid, Neural network constitutive for rate-dependent materials, Computers & Structures 84 (2006) 955-963.
• [4] S. H. Hsiang, J. L. Kuo, Applying ANN to predict the forming load and mechanical property of magnesium alloy under hot extrusion, The International Journal of Advanced Manufacturing Technology 26 (2005) 970-977.
• [5] S. H. Hsiang, J. L. Kuo, F. Y. Yang, Using artificial neural networks to investigate the influence of temperature on hot extrusion of AZ61 magnesium alloy, Journal of Intelligent Manufacturing 17/2 (2006) 191-201.
• [6] H. D. Liu, A. T. Tang, F. S. Pan, A model on the correlation between composition and mechanical properties of Mg-Al- Zn alloys by using artificial neural network, Materials Science Forum 488-489 (2005) 793-796.
• [7] A. T. Tang, B. Liu, F. S. Pan, J. Zhang, J. Peng, J. F. Wang, An improved neural network model for prediction of mechanical properties of magnesium alloys, Science in China Series E: Technological Sciences 52/1 (2009) 155-160.
• [8] L. A. Dobrzański, M. Król, T. Tański, R. Maniara, Effect of cooling rate on the solidification behaviour of magnesium alloys, Archives of Computational Materials Science and Surface Engineering 1/1 (2009) 21-24.
• [9] L. A. Dobrzański, M. Król, Thermal and mechanical characteristics of cast Mg-Al-Zn alloy, Archives of Foundry Engineering, 1/10 (2010) 27-30.
• [10] L. A. Dobrzański, T. Tański, J. Domagała, M. Król, Sz. Malara, A. Klimpel, Structure and properties of the Mg alloys in as-cast state and after heat and laser treatment, Journal of Achievements in Materials Science And Engineering 31/2 (2008) 123-147.
• [11] L. A. Dobrzański, S. Malara, J. Trzaska, Project of computer programme for designing the steel with the assumed CCT diagram, Journal of Achievements in Materials and Manufacturing Engineering 20 (2007) 351-354.
• [12] 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.
• [13] 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.
• [14] L. A. Dobrzański, T. Tański, J. Trzaska, L. Cížek, 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.
• [15] 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.
• [16] Method and Apparatus for Universal Metallurgical Simulation and Analysis – United States Patent, Patent No.: US 7,354,491 B2, Date of Patent: Apr. 8.