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Neural network application for prediction mechanical properties of Mg-Al-Zn alloys

Dobrzański L.A., Król M.

Archives of Computational Materials Science and Surface Engineering

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2010

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Vol. 2, nr 4

181-188

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: Original value of the work is applied the artificial intelligence as a tools for designing the required mechanical properties for Mg-Al-Zn castings.

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Application a neural networks in crystallization process of Mg-Al-Zn alloys

Dobrzański L.A., Król M., Tański T.

Archives of Computational Materials Science and Surface Engineering

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2010

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Vol. 2, nr 3

149-156

EN

Purpose: The purpose of this paper is presents a methodology to predict crystallization temperatures during solidify of metal obtained during crystallization process using an UMSA platform, based on cooling rate and chemical composition. 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. Temperatures were registered by supersensitive K-thermocouples. Findings: Research limitations/implications: The results of this investigation show that there is a high correlation between experimental and predicted dates and the neural networks have a great potential in crystallization process 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 and makes possibility to determine a crystallization temperatures based on chemical composition. Originality/value: Original value of the work is applied the artificial intelligence as a tools for designing the required mechanical properties for Mg-Al-Zn castings.

3

Finite Element Method application for modelling of mechanical properties

Dobrzański L.A., Śliwa A., Tański T.

Archives of Computational Materials Science and Surface Engineering

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2009

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Vol. 1, nr 1

25-28

EN

Purpose: A numerical model was developed in order to predict the hardness for casting the magnesium alloys MCMgAl12Zn1, MCMgAl6Zn1, MCMgAl3Zn1 and MCMgAl9Zn1. Design/methodology/approach: Computer simulation of hardness was carried out with the help of finite element method in ANSYS environment, and the experimental values of hardness were determined basing on the Rockwell method. Findings: The presented model meets the initial criteria, which gives ground to the assumption about its usability for determining the hardness in casting the magnesium alloys MCMgAl12Zn1, MCMgAl6Zn1, MCMgAl3Zn1 and MCMgAl9Zn1, employing the finite element method using the ANSYS program. The computer simulation results correlate with the experimental results. Research limitations/implications: Presently the computer simulation is very popular and it is based on the finite element method, which allows to better understand the interdependence between parameters of process and choosing optimal solution. Originality/value: The possibility of application faster and faster calculation machines and coming into being many software make possible the creation of more precise models and more adequate ones to reality.