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.

Abstract. The report is focused on determination of the material parameters from experimental data, which will be used as input data to computational modeling of radial tires for passenger vehicles. It is necessary to have knowledge about the geometry, material parameters, cross-section and structure of tire casing (number of layers of the belt and carcass, information about the bead and cap ply) for creation of computational models of tire casing for stress-strain analysis of a tire under vertical load, modal analysis etc. The tire casing parts in computational models are partially replaced with material and geometrical parameters with specified stiffness. The Finite Element Method using the program system ANSYS is applied to the computational modeling. The experiments of total tires are needed for verification analyses between computational results and experimental data. The experiments of parts of the tire casing such as parts of tread and next rubber parts of tire are needed for determination of the parameters of constitutive models of rubber elasticity such as the parameters of hyperelastic Mooney-Rivlin model. The report describes the result for two tires 165/65 R13 and 215/40 R17 as samples. The structures of the tire-crown with geometrical and material parameters are presented. The authors used orthotropic material parameters for determination of the structure parts of tire as the steel-cord belt. The values of modules of elasticity and Poisson ratios are presented for 165/65 R13. The orthotropic material parameters for definition of the tire-crown of 215/40 R17 are determined as input data to the computational model of tire.

In this study, a new generalized regression neural network model for predicting the curing characteristics of rubber blends with different contents of carbon black filler cured at various temperatures is proposed for the first time The carbon black contents in the rubber blend and cure temperature were used as input parameters, while the minimum and maximum elastic torque, scorch time, and optimal cure time, obtained from the analysis of 11 rheological cure curves registered at 10 various temperatures, were considered as output parameters of the model. A special pre-processing procedure of the experimental input and target data and the training algorithm is described. Less than 55% of the experimental data were used to significantly reduce the total number of input and target data points needed for training the model. Satisfactory agreement between the predicted and experimental data, with a maximum error in the prediction not exceeding 5%, was found. It is concluded that the generalized regression neural network is a powerful tool for intelligently modelling the curing process of rubber blends even in the case of a small dataset, and it can find a wide range of practical applications in the rubber industry.

The work has been supported by the Czech grant projects MPO FR-TI3/818 (sponsored by the Ministry of Industry and Trade of the Czech Republic) and by the Slovak-Czech grant project 7AMB12SK126 (sponsored by the Ministry of Education, Youth and Sports of the Czech Republic).

IntroductionThe paper deals with the specific composites with elastomer matrix and steel cord reinforcement. These composites are typical for a tire-casing for transport means. The experiments of tires are needed for verification analyses between computations and test data. The paper is oriented on static experiments on a special test machine for tires -static adhesor.