The paper deals with the innovative ways of nonstandard, simplifying applications of the valid method for evaluating uncertainties in measurement results and with the definition of conditions of their usability. The evaluation of a substitute criterion for measurement accuracy by means of a relative difference between the measurand and its reference value is proposed. This nonstandard relative uncertainty is comparable with the overall relative standard uncertainty in the measurement result, and thus the evaluation of it enables other simplifications in the calculations of measurement result uncertainties. The use of the simplified evaluation of measurement results is illustrated in two experiments in measurement of the coefficient of thermal conductivity of an insulating material newly developed for the needs of building practice, namely measurement using commercial instruments, and measurement using a newly developed original measuring instrument.
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 paper deals with the measurement and identification of surfaces after machining in a non-contact manner. It presents a new modified measurement method and its implementation, the results of intensity distribution in the defocusing plane, their analysis and interpretation. The scanned intensity distribution at the defocusing plane gives information necessary to assess the second derivatives, and thus, surface functions which can be used to determine groove curvatures of the real surface morphology. The proposed method of measurement has proved to be very sensitive in evaluating the differences between surface finishing methods by which the measured surface standards (etalons) were machined. Two methods of machining were chosen: face grinding and planning. By comparing the roughness standard values Ra, there were obtained relationships between these values and the parameter of the characteristic frequency of vertical inequality being measured according to the presented method. A good correlation between the measured and surface standard values with the correlation coefficient taking a range of values from 0.8 to 1 was achieved.
Austenitic stainless steel belongs to the best oxidation-resistant alloys, which must function effectively and reliably when used in a corrosion environment. Their attractive combination of properties ensures their stable position in the steel industry. They belong to a group of difficult-to-cut materials, and the abrasive water jet cutting technology is often used for their processing. Samples made of stainless steel AISI 304 has been used as the experimental material. Data generated during experiments were used to study the effects of AWJ process parameters (high-pressure water volume flow rate, the diameter of the abrasive nozzle, the distance of the nozzle from the material surface, cutting head feed rate, abrasive mass flow, and material thickness) on surface roughness. Based on the analysis and interpretation of all data, a prediction model was created. The main goal of the long-term research was to create the simplest and most usable prediction model for the group of austenitic steels, based on the evaluation of the practical results obtained in the company Watting Ltd. (Budovateľská 3598/38, Prešov, Slovakia) during 20 years of operation and cooperation with customers from industrial practice. Based on specific customer requirements from practice, the publication also contains specific recommendations for practice and a proposal for the classification of the predicted cut quality.
This paper from the field of environmental chemistry offers an innovative use of sorbents in the treatment of waste industrial water. Various industrial activities, especially the use of technological fluids in machining, surface treatment of materials, ore extraction, pesticide use in agriculture, etc., create wastewater containing dangerous metals that cause serious health problems. This paper presents the results of studies of the natural zeolite clinoptilolite as a sorbent of copper cations. These results provide the measurement of the sorption kinetics as well as the observed parameters of sorption of copper cations from the aquatic environment to the clinoptilolite from a promising Slovak site. The effectiveness of the natural sorbent is also compared with that of certain known synthetic sorbents.
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