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The aim of this paper is an attempt to answer the question of whether, on the basis of the values of the mechanical properties of ferritic stainless steels, it is possible to predict the chemical concentration of carbon and nine of the other most common alloying elements in these steels. The author believes that the relationships between the properties are more complicated and depend on a greater number of factors, such as heat and mechanical treatment conditions, but in this paper, they were not taken into account due to the uniform treatment of the tested steels. The modeling results proved to be very promising and indicate that for some elements, this is possible with high accuracy. Artificial neural networks with radial basis functions (RBF), multilayer perceptron with one and two hidden layers (MLP) and generalized regression neural networks (GRNN) were used for modeling. In order to minimize the manufacturing cost of products, developed artificial neural networks can be used in industry. They may also simplify the selection of materials if the engineer has to correctly select chemical components and appropriate plastic and/or heat treatments of stainless steel with the necessary mechanical properties.

Purpose: This article is presenting the Material Science Virtual Laboratory. Developed laboratory is an open scientific, investigative, simulating and didactic medium helpful in the realisation of the scientific and didactic tasks in the field of material Science. It is implemented in the Institute of Engineering Materials and Biomaterials of Silesian University of Technology in Gliwice, Poland. Design/methodology/approach: The laboratory is a set of testers and training simulators, set in the Virtuality and created in several languages and the programming techniques, which interprets the properties, functionality and manual rules of actual equipment installed and accessible in the real science labs of scientific universities. Findings: Application of the equipment, that is practically imperishable, cheap in exploitation and ease in the use encourages students and scientific workers to independent audits and experiments in places, where the possibilities of their performance in the real investigative laboratory will be restricted because of the high material costs, difficult access to real equipment or the possible peril of his impairment. Research limitations/implications: The proposed solutions allow the utilisation of the developed virtual environment as a new medium in both, the scientific work performed remotely, as well as in education during classes. Practical implications: The usage possibilities of the virtual laboratory are practically unrestricted; it can be a foundation for any surveys, course or training plan. Originality/value: The project of the virtual laboratory corresponds with the global tendency for expand the investigative and academic centres about the possibilities of training and experiments performance with use of the virtual reality. This enriches investigation and training programmes of the new abilities reserved so far exclusively for effecting only on actual equipment.

This paper presents the application of artificial neural networks for prediction contact resistance of front metallization for silicon solar cells. The influence of the obtained front electrode features on electrical properties of solar cells was estimated. The front electrode of photovoltaic cells was deposited using screen printing (SP) method and next to manufactured by two methods: convectional (1. co-fired in an infrared belt furnace) and unconventional (2. Selective Laser Sintering). Resistance of front electrodes solar cells was investigated using Transmission Line Model (TLM). Artificial neural networks were obtained with the use of Statistica Neural Network by Statsoft. Created artificial neural networks makes possible the easy modelling of contact resistance of manufactured front metallization and allows the better selection of production parameters. The following technological recommendations for the screen printing connected with co-firing and selective laser sintering technology such as optimal paste composition, morphology of the silicon substrate, co-firing temperature and the power and scanning speed of the laser beam to manufacture the front electrode of silicon solar cells were experimentally selected in order to obtain uniformly melted structure well adhered to substrate, of a small front electrode substrate joint resistance value. The prediction possibility of contact resistance of manufactured front metallization is valuable for manufacturers and constructors. It allows preserving the customers’ quality requirements and bringing also measurable financial advantages.