Ivan Ružiak

The Study of Electrical Transport in Rubber Blends Filled by Single Wall Carbon Nanotubes

The paper deals with the electrical and mechanical properties of rubber blends filled by single wall carbon nanotubes (SWCNT-0,6 weight %). We have investigated the alternating current resistivity (ACR), real and imaginary part of dielectric constant,and loss factor .The addition of (SWCNT) to a rubber blend decreases ACR and increases constants ,and loss factor .

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.

The Chosen Aspects of Materials and Construction Influence on the Tire Safety

Security of the road transport depends on the quality of basic and applied research concerning materials and internal construction of tires. The design shapes and material properties characterized by low hysteretic losses as well as a construction have an influence on the driving comfort, adhesion, wear resistance and fatigue resistance. Thick fibre reinforced composites are used extensively in rubber products such as tires and conveyer belts. Generally, the reinforced parts of rubber products on a sub macroscopic level are highly heterogeneous and anisotropic because they are composed of rubber compounds, and textile and steel cords. Rubber compounds consist of natural or synthetic rubber, carbon black, curing agents, cure accelerators, plasticizers, protective agents and other ingredients.

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.

Artificial Neural Network Modelling of Glass Laminate Sample Shape Influence on the ESPI Modes

The present work is devoted to the applications of artificial neural networks (ANN) for material design prediction. We have investigated the dependence of the generated mode frequency as a function of a sample thickness and a sample shape of glass laminate samples by electronic speckle interferometry (ESPI). The obtained experimental results for differently shaped (thickness, canting and rounding) glass laminate samples are compared with those of ANN. The coincidence of both experimental and simulated results is very good.

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.

Injection Moulding Versus Rapid Prototyping—Thermal and Mechanical Properties

This paper is devoted to the comparison of the influence of injection moulding (IM) on heat transport phenomena and the mechanical properties of materials, with rapid prototyping (RP). The tested materials included ABS and polyurethane (PU). The results show decrease of both mechanical properties and thermal transport phenomena in the case of RP samples.

Application of Artificial Neural Networks in Chosen Glass Laminates Properties Prediction

The article deals with applications of the artificial neural networks at the evaluation of chosen material’s properties (sample thickness, sample shape) measured by electronic speckle pattern interferometry. We have investigated the dependence of the generated mode frequency as a function of sample thickness as well as the sample shape of glass laminate samples. Obtained experimental results for differently shaped glass laminate samples are compared with those of artificial neural networks and finite element method simulation. The coincidence of both experimental and simulated results is very good.

Thermo-Mechanical Analysis of Rubber Compounds Filled by Carbon Nanotubes

The presented work is devoted to the investigation of the influence of single wall carbon nanotubes (SWCNT) on the thermal and mechanical properties of rubber blends. The materials under investigation were rubber blends filled by carbon black (CB-labelled as standard or S) or a mixture of CB with single wall carbon nanotubes (0.6 wt.% of SWCNT). The thermal parameters of the rubber samples were measured on the basis of an exponential model of a cooling body (developed in a lumped capacity model). From the measured cooling curve of the sample after its deformation, the specific heat capacity cp [J/kg/, thermal diffusivity α [m2/ and thermal conductivity k [W/m/K] were measured. The increase of α and k was observed for rubber blends filled with a mixture of CB and SWCNT. The complex thermo mechanical analyzer (CTMA) was used for determination of thermal and mechanical parameters. The static Youngs modulus shows an immense increase after SWCNT addition. The dynamic Youngs modulus also shows an increase after SWCNT introduction. A simultaneous tgδ decrease caused by SWCNT presence is observed in this case.

Influence of Thermal Treatment on the Heat Transfer of 10GN2MFA Steel

Three samples of 10GN2MFA steel were thermally treated at quenching temperatures equal to 900°C, 1000°C and 1100°C, and temperature of the tempering was 670°C. Thermo-physical properties of the steel were modelled by lumped capacity model. The sample was mechanically stressed under yield strength and then the cooling curve of stressed material on air was measured. The heat transfer through grips was taken into account. Thermo-physical properties were obtained by parametric fitting of time-temperature data obtained from cooling curve. Both thermal diffusivity and thermal conductivity increase with the quenching temperature. Specific heat capacity of steel samples after thermal treatment does not change significantly. All the measured thermo-physical parameters were in good agreement with the table values for low carbon Ni steels.