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Abstract

A determination of the heating degree of the moulding sand with bentonite on the grounds of simulating investigations with the application of the MAGMA program, constitutes the contents of the paper. To this end the numerical simulation of the temperature distribution in the virtual casting mould was performed. It was assumed that the mould cavity was filled with a moulding sand with bentonite of a moisture content 3,2 % and bentonite content 8 %. A computer simulation can be used for predicting the heating degree of moulding sands with bentonite. Thus, prediction of the active bentonite (montmorillonite) content in individual layers of the overheated moulding sand can be done by means of the simulation. An overheating degree of a moulding sand with bentonite, and thus the bentonite deactivation depends on a temperature of a casting alloy, casting mass, ratio of: masssand : masscasting, moulding sand amount in the mould and contact area: metal – mould (geometry of the casting shape). Generally it can be stated, that the bentonite deactivation degree depends on two main factors: temperature of moulding sand heating and time of its operation.
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Abstract

The aim of this work is to develop a numerical model capable of predicting the grain density in the Mg-based matrix phase of an AZ91/SiC composite, as a function of the total mass fraction of the embedded SiC particles. Based on earlier work in a range of alloy systems, we assume an exponential relationship between the grain density and the maximum supercooling during solidification. Analysis of data from cast samples with different thicknesses, and mass fractions of added SiCp, permits conclusions to be drawn on the role of SiCp in increasing grain density. By fitting the data, an empirical nucleation law is derived that can be used in a micro model. Numerical simulation based on the model can predict the grain density of magnesium alloys containing SiC particles, using the mass fraction of the particles as inputs. These predictions are compared with measured data.
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