Mechanical vibration is a phenomenon that occurs very often in the process of metal cutting. It is one of many factors that have a significant impact on the quality of machining and thus prevent a ...rise in productivity. These vibrations are undesirable not only because they cause noise and dynamic stresses, which result in fatigue and failure of the machine, but also energy loss or performance reduction. An experimental analysis of mechanical vibrations in the machining process is important because vibrations may cause spindle damage, machine tool wear or workpiece wear and may be left with poor surface finish. Therefore, this material is devoted to the study of the impact of input cutting parameters—spindle speed and depth of cut on the formation and size of mechanical vibrations on lathe bearing housings. The measured experimental values of the acceleration amplitude of mechanical vibrations on bearing housings were compared with theoretical values.
The paper presents the results of research concerning the development of a new forecasting methodology, which has finally allowed us to solve the urgent problem of determining the individual resource ...of any technical systems, which has long been waiting for its solution. The solution to this problem is of particular importance for small series or single objects of inspection. This circumstance determines the relevance of the material presented in the paper. The work aims to develop a new methodology for forecasting the individual resource of technical systems, including unique and small series ones. A new methodology for forecasting the individual resource of technical systems is proposed, based on the identification of the trend model of the monitored parameter, compiled based on the results of regular monitoring of the technical condition of various industrial equipment, including small series or single pieces only. The trend model coefficients determined during the identification process are used for calculation of the required resource of the machine. The methodology of individual resource forecasting and evaluation based on this degree of criticality of the technical condition of industrial equipment, including unique and low series equipment, was implemented in a software product and used in predicting the resource of a centrifugal pump. The approbation of the proposed forecasting methodology confirmed the effectiveness and efficiency of the software created on its basis, which allows us to recommend the methodology and software for practical use in solving problems of predicting the resource and diagnosing the technical condition of various industrial equipment. Prospects for further research consist in hardware implementation based on stationary, mobile and embedded control systems of the developed methodology for predicting the individual resource of mechanical systems.
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.
In the present study, real topographic function and maximal depth of neglected initial zone were analytically developed to predict surface roughness on the top region of surfaces created by abrasive ...waterjet. An upper area of workpieces was analysed in details. Experimentally created surfaces were measured by HOMMEL TESTER T8000 and non-contact profilometer Micro Prof FRT. As an experimental material, stainless steel AISI 304, AISI 309 and aluminium with a thickness of 10 mm have been used. On the basis of analysis and interpretation of data obtained from the surface, a topography function
Ra
d
, which is necessary to be known for the subsequent prediction and control of abrasive waterjet cutting technology, is derived. In the framework of interpretation of measured values, relations among these parameters are systematically analysed and physico-mechanical and distributional principles governing these parameter are formulated newly. Results are very important for further estimation of analytical expression of the real topographic function for any surface created by abrasive waterjet cutting.
Phase Change Material (PCM) is mainly used in thermal energy storage. The addition of small PCM particles to the working fluid circulating in the heat exchange systems allowed to increase the amount ...of transported energy thanks to the use of latent heat—the heat of phase change. Encapsulating PCM in microcapsules avoids the disadvantages of PCM emulsions and makes the resulting slurry an attractive heat energy carrier. The paper presents the effect of the aggregate state of PCM enclosed in microcapsules on the flow resistance of the slurry through a rectilinear tubular channel. The tests were carried out with the use of a tube with an internal diameter of 4 mm and a measuring section length of 400 mm. A slurry of 21.5 wt.% PCM microcapsules (MPCM) was used as the working fluid in distilled water. A slurry with temperatures of 18.4 °C (PCM encapsulated in a solid state), 26.1 °C (PCM is in a phase change), and 30.5 °C (PCM in a liquid state) flowed through the measuring section. The mass flow rate of the MPCM slurry reached 70 kg/h (Remax = 2150). It was shown that the higher the Re number, the higher the value of the flow resistance, and the more clearly this value depended on the temperature of the slurry. Detailed analyses indicate that the observed changes were not the result of a change in the viscosity of the slurry, but its density depending on the state of the PCM. Significant changes in the density of the slurry in the range of the phase transition temperature are the result of significant changes in the volume of the microcapsule containing the phase change material in different aggregate states.
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.
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.
This work evaluates the possibility of identifying mechanical parameters, especially upper and lower yield points, by the analytical processing of specific elements of the topography of surfaces ...generated with abrasive waterjet technology. We developed a new system of equations, which are connected with each other in such a way that the result of a calculation is a comprehensive mathematical-physical model, which describes numerically as well as graphically the deformation process of material cutting using an abrasive waterjet. The results of our model have been successfully checked against those obtained by means of a tensile test. The main prospect for future applications of the method presented in this article concerns the identification of mechanical parameters associated with the prediction of material behavior. The findings of this study can contribute to a more detailed understanding of the relationships: material properties-tool properties-deformation properties.
The precise experimental estimation of mechanical properties of rubber blends can be a very costly and time-consuming process. The present work explores the possibilities of increasing its efficiency ...by using artificial neural networks to study the mechanical behavior of these widely used materials. A multilayer feed-forward back-propagation artificial neural network model, with a strain and the carbon black content as input parameters and stress as an output parameter, has been developed to predict the uniaxial tensile response of vulcanized natural rubber blends with different contents of carbon black in the form of engineering stress-strain curves. A novel procedure has been created for the simulation of the optimized artificial neural network model with input datasets generated by a regression model of an experimental dependence of tensile strain-at-break on the carbon black content in the investigated blends. Errors of the prediction of experimental stress-strain curves, as well as of tensile strain-at-break, tensile stress-at-break and M100 tensile modulus were estimated for all simulated stress-strain curves. The present study demonstrated that the performance of a developed neural network model to predict the stress-strain curves of rubber blends with different contents of carbon black is also exceptionally high in the case of a network that had never learned the input data, which makes it a suitable tool for extensive use in practice.
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