Jiří David

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.

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.

On Shearography Testing of Tires Separations

The paper deals with the systematic experimental study of tires wear accompanied by separation created in tires of different construction exposed to dynamic loading. 12 pieces of tires with the dimensions of 225/75 R 16 C with the same tread pattern were manufactured for the test in order to examine the impact on the material and design changes on the spread of separations in a tire. The atlas of separation is presented. The results of separation detection obtained by contact-less shearography are compared with those of optical. Graphic relevant analysis of tire wearing process is effectuated.

Lumped Capacitance Model in Thermal Analysis of Solid Materials

The paper is devoted to the presentation of a method for measurement of thermal conductivity k, specific heat capacity cp and thermal diffusivity α applying the lumped capacitance model (LCM) as a special case of Newtons model of cooling. At the specific experimental conditions resulting from the theoretical analysis of the used model, we present relatively very precise method for experimental determination of all three above mentioned thermal parameters for materials with different thermal transport properties. The input experimental data provide a cooling curve of the tested material obtained in special experimental arrangement. The evaluation of experimental data is realized by software the fundamental features of which are presented here. The statistical analysis of experimental data was performed (99% confidence interval P99).

Neural Model for Prediction of Tires Eigenfrequencies

The work is devoted to the application of an artificial neural network (ANN) to analyze eigenfrequencies of personal tires of different construction. Experimental measurements of personal tire eigenfrequencies by electronic speckle interferometry (ESPI) are compared with those previewed by ANN. Very good agreement of both data sets is presented.