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.

Iron is an essential trace element, but at high doses, this element may pose a health risk. Wastewater from iron ore mining, steel production, and metal processing, among other heavy metals, also contains high concentrations of iron (Fe³⁺). The use of sorption on natural materials is a potential alternative to conventional methods for removing iron ions, also because of low cost. The methods presented in this article are based on the study of kinetic properties and the acquisition of adsorption isotherms, which are one of the most important characteristics of adsorption mechanisms. The course of sorption is analyzed according to the Freundlich sorption isotherm model. Isotherm parameters are evaluated using experimental results of ferric cation sorption. The results presented relate to the investigation of natural zeolite-clinoptilolite as a ferric cation sorbent, providing a measurement of the sorption kinetics as well as the observed sorption parameters of iron cations from aqueous media. The optimal time for equilibrium in the adsorption system is determined from the kinetic dependencies. The dependence of the achieved equilibrium concentration on the initial concentration of the solution was also expressed, both graphically and analytically. The new prediction model was compared with the traditional Freundlich model. Finally, adsorption isotherms tested under laboratory conditions for a practical application can be recommended for the preliminary examination of the possible technological use of natural zeolite in the wastewater treatment process.

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 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.

This study investigates the effects of post-curing temperatures on the tensile properties of hybrid basalt-jute-glass-carbon fiber-reinforced polymers (FRPs). Composite specimens were post-cured at 60 °C and 100 °C for 60 min, and their tensile behavior was assessed using a servo-hydraulic testing machine. Numerical simulations using the Abaqus software V6.14 were also conducted to compare experimental and computational results. The findings indicate that post-curing heat treatment enhances ductility due to increased polymer cross-linking, but excessive heat treatment at 100 °C negatively impacts elongation at fracture. The results revealed that specimens post-cured at 60 °C exhibited the optimal balance between strength and ductility, with increased elongation and moderate tensile strength. However, at 100 °C, while tensile strength improved in some cases, a significant decrease in elasticity and an increased risk of brittleness were observed, suggesting that extreme heat treatment may degrade polymer integrity. Natural fiber composites, particularly jute-based samples, outperformed synthetic composites in terms of elongation and overall mechanical stability. The numerical simulations provided further insights but showed discrepancies with experimental results, mainly due to fiber property variations and fabric waviness, underscoring the challenges of accurately modeling woven composites. The study highlights the importance of controlled post-curing temperatures in optimizing the mechanical performance of FRP composites, with 60 °C identified as the most effective condition for achieving a favorable balance between tensile strength, flexibility, and material durability. These findings offer valuable insights for material scientists and engineers working on the development of high-performance composite materials for structural and industrial applications.