Ivan Kopal

Advanced Fillers Enhancing Thermal and Mechanical Properties of Rubber Blends

In the paper we present measurements of transport physical parameters such as thermal conductivity, diffusivity and specific heat capacity and dc electrical conductivity as well as the mechanical values E*, tg δ for rubber compounds filled by different ratio of silica - carbon black fillers. From presented results it is possible to see that proper filler concentration (rubber blend - silica - carbon black) rising all thermal parameters as well as mechanical properties represented by complex Young’s modulus and so, maintains the good mechanical parameters of the blend and finally it also lowers the electrical resistance. All trends are favourable for the improvement of useful rubber blends properties.

Weibull distribution application on temperature dependence of polyurethane storage modulus

Abstract In this paper, a stiffness–temperature model based on Weibull statistics was applied to quantitatively describe changes in the storage modulus of thermoplastic polyurethane over a wide range of temperature. The variation of the storage modulus with temperature was obtained from dynamic mechanical analysis tests across transition temperatures. Both the physical and statistical parameters of the applied model were estimated in the process of parametric fitting of the model to the storage modulus versus a temperature curve by using a trust region algorithm for a robust nonlinear least squares method. Good agreement between the modeled and experimental data has been found over the entire investigated temperature range, including all observed relaxation transitions.

Artificial Neural Networks Prediction of Rubber Mechanical Properties in Aged and Nonaged State

Artificial neural networks (ANN) have been used for characterization of rubber blend mixtures ageing and for prediction of mechanical properties according to chemical composition. Strength Rm and modulus M100 have been evaluated. The ANN application was tested by statistical function RMSE (root mean square error) and R2 (coefficient of determination) which value for all predictions was higher than 0.93.

Modeling the Temperature Dependence of Dynamic Mechanical Properties and Visco-Elastic Behavior of Thermoplastic Polyurethane Using Artificial Neural Network

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.

Measuring the Thermal Characteristics of Concretes Exposed to Extreme Conditions

This article presents the results of an experimental research dealing with the measurement of the thermal characteristics of concretes based on natural and artificial aggregates (steel slag). The samples of concrete composites were prepared on the basis of natural aggregate fractions 0/4, 4/8 and 8/16 mm and on the basis of steel slag fr. 4/8 mm. The volume ratio of the individual aggregate fractions in all experimental mixtures used for the production of concrete composites was 40:30:30 (fr. 0/4: 4/8: 8/16). The prepared samples of concrete composites based on natural aggregate and natural aggregate combined with steel slag were subjected to the tests of strength characteristics, water-tightness, thermal characteristics using a commercial device ISOMET 2104 (measurement of the coefficient of thermal conductivity λ, specific heat capacity c, and the coefficient of thermal diffusivity a), and heating in a prototype calorimetric computer-controlled chamber. The main attention was focused on the testing of the value changes of the coefficients of thermal conductivity λ depending on the changes of temperatures within the range of -5 °C to + 40 °C. The measurements of these thermal characteristics have very high informative value, especially because these material parameters are not tabulated for the newly designed building materials, and that is why they are not examined at extreme temperatures. This is a reason why they cannot be used as important data during the thermal calculations of a non-insulated concrete structure (e.g. using polystyrene and / or glass wool).

The Influence of Rubber Blend Aging and Sample Homogeneity on Heat Transport Phenomena

This paper is devoted to the study of thermal transport phenomena changes caused by natural aging of rubber blends. Thermal conductivity, diffusivity and heat capacity of rubber blends were measured and compared for the same samples before and after half of a year. Samples were stored at room temperature and daily light. In the frame of our investigations there has been observed the decrease of the thermal diffusivity as well as the thermal conductivity in the interval approximately 40-50 %. The changes of specific heat capacity after sample aging were negligible. The explanation of such behaviour we can see in the sample structure degradation caused by the environmental influence.

Prediction of the Tensile Response of Carbon Black Filled Rubber Blends by Artificial Neural Network

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.

Chosen Electrical Properties of Montmorillonite/Polyaniline Composites

The paper deals with the electrical properties of Montmorillonite (MMT)/Polyaniline (PANI) composites. These materials show specific electrical properties as relatively high anisotropic electrical conductivity, pressure dependent electrical resistance with relatively high hysteresis. Metallographic analyse as well as the Vickers micro-hardness dsof sample surfaces is presented. All these properties predetermine this material as an interesting piezoresistor.

Modifications of Viscoelastic Properties of Natural Rubber/Styrene-Butadiene Rubber Blend by Electron Beam Irradiation

The influence of high-energy electron beam irradiation on the viscoelastic properties of natural rubber/styrene-butadiene rubber blend has been investigated in the study presented in this paper. Changes in viscoelastic properties were studied as a function of radiation dose and temperature using the dynamic mechanical analysis in the temperature range from 10 to 240 °C at frequency of 0.5 Hz. The samples of material under the investigation were irradiated in the presence of air, at room temperature, using the 5 meV electron beam in the dose range from 50 to 300 kGy, with the maximum beam power of 50 W. The experimental results have shown that an increase in the radiation dose leads to an increase in the storage modulus and a corresponding decrease in the damping factor. With increasing radiation dose, the curing process of tested rubber blend begins and ends at lower temperatures, with a higher initial and final storage modulus and a lower initial damping factor, whereas the radiation dose has almost no impact on the final value of damping factor. The unified regression model describing analytically the dependence of all monitored properties of tested rubber blend on radiation dose with a high level of reliability was found as well using the multi-parametric fitting technique by a trust region algorithm of a nonlinear least-squares method.

Identification of Upper and Lower Level Yield Strength in Materials

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.