This article discusses the results of studies using the developed artificial neural networks in the analysis of the occurrence of the four main mechanisms destroying the selected forging tools subjected to five different surface treatment variants (nitrided layer, pad welded layer and three hybrid layers, i.e. AlCrTiSiN, Cr/CrN and Cr/AlCrTiN). Knowledge of the forging tool durability, needed in the process of artificial neural network training, was included in the set of training data (about 800 records) derived from long-term comprehensive research carried out under industrial conditions. Based on this set, neural networks with different architectures were developed and the results concerning the intensity of the occurrence of thermal-mechanical fatigue, abrasive wear, mechanical fatigue and plastic deformation were generated for each type of the applied treatment relative to the number of forgings, pressure, friction path and temperature.
Constantly developing nanotechnology provides the possibility of manufacturing nanostructured composites with a polymer matrix doped with ceramic nanoparticles, including ZnO. A specific feature of polymers, i.e. ceramic composite materials, is an amelioration in physical properties for polymer matrix and reinforcement. The aim of the paper was to produce thin fibrous composite mats, reinforced with ZnO nanoparticles and a polyvinylpyrrolidone (PVP) matrix obtained by means of the electrospinning process and then examining the influence of the strength of the reinforcement on the morphology and optical properties of the composite nanofibers. The morphology and structure of the fibrous mats was examined by a scanning electron microscope (SEM) with an energy dispersive spectrometer (EDS) and Fourier-transform infrared spectroscopy (FTIR). UV –Vis spectroscopy allowed to examine the impact of zinc oxide on the optical properties of PVP/ZnO nanofibers and to investigate the width of the energy gap.
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