In this work synthesis, sintering processes and properties of three groups of perovskite-type ceramics utilized in chosen electronic applications are briefly described. The first group includes high permittivity dielectrics based on relaxor ferroelectrics and new leadfree ceramics, destined for bulk and thick film capacitors. The second group comprises ceramics for low and high temperature thermistors and the third one nonstoichiometric conducting compounds containing doped SrMnO3 and SrCoO3, tested as electrode materials for solid state cells.
In the paper the use of the artificial neural network to the control of the work of heat treating equipment for the long axisymmetric steel elements with variable diameters is presented. It is assumed that the velocity of the heat source is modified in the process and is in real time updated according to the current diameter. The measurement of the diameter is performed at a constant distance from the heat source (∆z = 0). The main task of the model is control the assumed values of temperature at constant parameters of the heat source such as radius and power. Therefore the parameter of the process controlled by the artificial neural network is the velocity of the heat source. The input data of the network are the values of temperature and the radius of the heated element. The learning, testing and validation sets were determined by using the equation of steady heat transfer process with a convective term. To verify the possibilities of the presented algorithm, based on the solve of the unsteady heat conduction with finite element method, a numerical simulation is performed. The calculations confirm the effectiveness of use of the presented solution, in order to obtain for example the constant depth of the heat affected zone for the geometrically variable hardened axisymmetric objects.