181 © Copyright by International OCSCO World Press. All rights reserved. 2010 2010 • Volume 2 • Issue 4 • 181-188 International Scientific Journal published quarterly by the Association of Computational Materials Science and Surface Engineering ARCHIVES of Computational Materials Science and Surface Engineering Neural network application for prediction mechanical properties of Mg-Al-Zn alloys L.A. Dobrzański*, M. Król Division of Material 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 * Corresponding author: E-mail address: leszek.dobrzanski@polsl.pl Received in a revised form 23.09.2009 ABSTRACT 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. Keywords: Numerical techniques; Neural networks; Mechanical properties; Magnesium alloys Reference to this paper should be given in the following way: L.A. Dobrzański, M. Król, Neural network application for prediction mechanical properties of Mg-Al-Zn alloys, Archives of Computational Materials Science and Surface Engineering 2/4 (2010) 181-188. ENGINEERING MATERIALS PROPERTIES 1. Introduction A strong emphasis on environmental protection all over the world in recent years has led to an increasing interest in topics on low energy consumption and product recycling. Magnesium alloy has a density of 1.74 g/cm 3 , which is less than the 2.7 g/cm 3 of aluminum alloy and other iron and steel materials. Magnesium metal for structural applications is processed into casting (die, sand, permanent mould and investment), extrusions, forgings, impact extrusions and flat rolled products. Castings far exceed cast and wrought products for reasons which will be discussed later and die castings account for 70% of the castings shipped; with demand for 1. Introduction