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The purpose of the work was to create an educational game to familiarize the user with the methodology of preparing a material sample for light microscopy. The goal of the game is to obtain a correct preparation of the sample, the microstructure of which can be observed under a light microscope. The game was developed in the Unity environment. All three-dimensional machine models, along with the necessary virtual environment and the gameplay scenario, were created. Due to the use of virtual reality, it has become possible to teach students how to use preparation devices without the need for the physical presence of students in the laboratory in an attractive and safe way. Failure to play the game will not damage material, or equipment, there is no risk to the user’s health. The game has been developed and is available in the Department of Engineering Materials and Biomaterials of the Faculty of Mechanical Engineering of the Silesian University of Technology. The form of a 3D game used in this study is an interesting alternative to traditional teaching aids. It can be used not only by students but also teachers and other people who want to broaden their knowledge about the functioning and methods of operation of laboratory equipment.

Purpose: The paper describes the use of artificial neural networks to research and predict the effect of chemical components and thermal treatment conditions on stainless steel's mechanical characteristics optimized by genetic algorithm. Design/methodology/approach: The quantity of input variables of artificial neural networks has been optimized using genetic algorithms to enhance the prediction quality of artificial neural network and to enhance their efficiency. Then a computational model was trained and evaluated with optimized artificial neural networks. Findings: Optimization, with the exception of tensile strength, has enabled the creation of artificial neural networks, which either showed a better or similar performance from base networks, as well as a decreased amount of input variables As a consequence, noise data is decreased in the computational model built with the use of these networks. Research limitations/implications: Data analysis was required to confirm the relevance of obtaining information used for modelling to use in training procedures for artificial neural networks. Practical implications: Using artificial intelligence enables the multi-faceted growth of stainless steel engineering, even though there is only a relatively small amount of descriptors. Built and optimized computational model building using optimized artificial neural networks enables prediction of mechanical characteristics after normalization of forged ferritic stainless steels. Originality/value: In order to decrease production expenses of products, an introduced model can be obtained in manufacturing industry. It can also simplify the selection of materials if the engineer has to correctly choose chemical elements and appropriate plastics and/or heat processing of stainless steels, having the necessary mechanical characteristics.

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.