In the paper, the Scanning Electron Microscopy (SEM) with Energy Dispersive X-ray Spectroscopy (EDS) and X-ray Photoelectron Spectroscopy (XPS) results of the surface layer formed on pure titanium after plasma electrolytic oxidation (micro arc oxidation) at the voltage of 450 V are shown. As an electrolyte, the mixture of copper nitrate Cu(NO₃)₂ (10–600 g/L) in concentrated phosphoric acid H₃PO₄ (98 g/mol) was used. The thickness of the obtained porous surface layer equals about 10 μm, and it consists mainly of titanium phosphates and oxygen with embedded copper ions as a bactericidal agent. The maximum percent of copper in the PEO surface layer was equal to 12.2 ± 0.7 wt % (7.6 ± 0.5 at %), which is the best result that the authors obtained. The top surface layer of all obtained plasma electrolytic oxidation (PEO) coatings consisted most likely mainly of Ti₃(PO₄)₄∙nH₃PO₄ and Cu₃(PO₄)₂∙nH₃PO₄ with a small addition of CuP₂, CuO and Cu₂O.

This paper presents one of the soft computing methods, specifically the artificial neural network technique, that has been used to model the temperature dependence of dynamic mechanical properties and visco-elastic behavior of widely exploited thermoplastic polyurethane over the wide range of temperatures. It is very complex and commonly a highly non-linear problem with no easy analytical methods to predict them directly and accurately in practice. Variations of the storage modulus, loss modulus, and the damping factor with temperature were obtained from the dynamic mechanical analysis tests across transition temperatures at constant single frequency of dynamic mechanical loading. Based on dynamic mechanical analysis experiments, temperature dependent values of both dynamic moduli and damping factor were calculated by three models of well-trained multi-layer feed-forward back-propagation artificial neural network. The excellent agreement between the modeled and experimental data has been found over the entire investigated temperature interval, including all of the observed relaxation transitions. The multi-layer feed-forward back-propagation artificial neural network has been confirmed to be a very effective artificial intelligence tool for the modeling of dynamic mechanical properties and for the prediction of visco-elastic behavior of tested thermoplastic polyurethane in the whole temperature range of its service life.

The aim of the work is find out influence of feed rate on the quality for the machined surface, especially surface roughness. The experiment was carried out by cutting tool HURCO VMXt30 with respect to the following cutting parameter: cutting speed 250; 350; 450; 550 and 850 m^.min^-1, feed 0,1; 0,5 and 1,0 mm/tooth. For testing was used two most common materials in the steel grade 1.1191 (12 050) and 1.2343 (19 552), and we chose two ordinary parameters of surface roughness; Ra - arithmetical mean roughness deviation and Rz - the largest inequalities height profile in the transverse direction and the longitudinal direction of machining. The structural equations will be the result which can describe the character depending on the cut surface parameters.

The presented work deals with the creation of a new radial basis function artificial neural network-based model of dynamic thermo-mechanical response and damping behavior of thermoplastic elastomers in the whole temperature interval of their entire lifetime and a wide frequency range of dynamic mechanical loading. The created model is based on experimental results of dynamic mechanical analysis of the widely used thermoplastic polyurethane, which is one of the typical representatives of thermoplastic elastomers. Verification and testing of the well-trained radial basis function neural network for temperature and frequency dependence of dynamic storage modulus, loss modulus, as well as loss tangent prediction showed excellent correspondence between experimental and modeled data, including all relaxation events observed in the polymeric material under study throughout the monitored temperature and frequency interval. The radial basis function artificial neural network has been confirmed to be an exceptionally high-performance artificial intelligence tool of soft computing for the effective predicting of short-term viscoelastic behavior of thermoplastic elastomer systems based on experimental results of dynamic mechanical analysis.

The efficient use of municipal waste is becoming increasingly important for sustainable environmental quality. An integral part of the process of recycling waste material is its quality sorting. Nowadays, both manual and automated sorting methods, especially optical sorting, are used worldwide for sorting mixed waste. This article presents the application of the NIR/VIS optical sorting system through a description of this processing equipment integrated into a specific plastic waste processing line. The aim of the article was to select the optimal mode of operation generally applicable to mixed waste, and, specifically, to compare the quality of sorting of plastic waste in automated and manual sorting modes. In order to compare the efficiency of the sorting system, significant parameters of the sorting process were proposed. The measurement of the plastic waste sorting purity parameter was based on data obtained by weighing selected control samples, and the evaluation of the results of these measurements showed, in particular, the dependence of the concerned parameter not only on the sorting mode, i.e., automated and manual, but also on the conveyor belt speed/waste material flow, on the pre-sorting methods and, in the case of manual sorting, on the operating shift. The subsequent economic assessment of both sorting processes clearly declared the investment in automated sorting systems in the optical system operation mode not only as profitable, but also as extremely advantageous both for the production economy and the environment.

The results of the investigations into the technological formation of new wear-resistant polymer composites based on polytetrafluoroethylene (PTFE) filled with disperse synthetic and natural compounds are presented. The efficiency of using PTFE composites reinforced with carbon fibers depends on many factors, which influence the significant improvement of physicomechanical characteristics. The results of this research allow stating that interfacial and surface phenomena of the polymer–solid interface and composition play a decisive role in PTFE composites properties. Fillers hinder the relative movement of the PTFE molecules past one another and, in this way, reduce creep or deformation of the parts, reducing the wear rate of parts used in dynamic applications as well as the coefficient of thermal expansion. The necessary structural parameters of such polymer composites are provided by regimes of process equipment.