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.

Modelling the flow properties of rubber blends makes it possible to predict their rheological behaviour during the processing and production of rubber-based products. As the nonlinear nature of such complex processes complicates the creation of exact analytical models, it is appropriate to use artificial intelligence tools in this modelling. The present study was implemented to develop a highly efficient artificial neural network model, optimised using a novel training algorithm with fast parallel computing to predict the results of rheological tests of rubber blends performed under different conditions. A series of 120 real dynamic viscosity–time curves, acquired by a rubber process analyser for styrene–butadiene rubber blends with varying carbon black contents vulcanised at different temperatures, were analysed using a Generalised Regression Neural Network. The model was optimised by limiting the fitting error of the training dataset to a pre-specified value of less than 1%. All repeated calculations were made via parallel computing with multiple computer cores, which significantly reduces the total computation time. An excellent agreement between the predicted and measured generalisation data was found, with an error of less than 4.7%, confirming the high generalisation performance of the newly developed model.

This paper presents an intelligent predictive system designed to support real-time decision making in the control of rubber blend mixing processes. The core of the system is a General Regression Neural Network (GRNN), which accurately predicts key process parameters, such as viscosity (expressed as torque), temperature, and energy consumption across varying masses of the processed material. The model can evaluate the mixing progress based on the initial 10% of input data, allowing early intervention and process optimisation. Experimental validation was conducted using a Brabender Plastograph EC Plus with a natural rubber-based blend in the mass range of 60–75 g. The GRNN kernel width parameter (σ) was optimised through a 10-fold cross-validation. High predictive accuracy was confirmed by values of the coefficient of determination (R²) approaching 1, and consistently low values of the root mean square error (RMSE). This system offers a robust and scalable solution for intelligent process control, productivity enhancement, and quality assurance across diverse industrial applications, beyond rubber blending.

This study focuses on the possibility of substituting the conventional carbon black filler N339 in a rubber compound, which also contains an energy-gypsum filler obtained as an industrial by-product, with an alternative carbon filler produced by pyrolysis of rubber waste. The proposed basic rubber compound recipe demonstrated excellent mechanical properties, which were also verified through industrial tests. Practical application in transport systems confirmed the reliability, durability, and robustness of the compound ensuring long-term functionality even in demanding operating conditions. From an ecological point of view, the substitution of conventional fillers with pyrolytic carbon materials is being pursued to reduce the environmental burden of the material. The rheological and vulcanization characteristics of the compounds were subject to minimal changes of up to 8%. The increase in the scorch time value of the compounds can be positively assessed given that the optimal vulcanization time has decreased, which leads to a faster course of the vulcanization process. The most significant effect was a 56% reduction in tensile strength without significant effect on elongation at break. These results document the potential for complete replacement of traditional fillers in rubber systems while maintaining several functional parameters.

The effect of filler type and quantity (30, 40 phr), i.e., walnut shell powder (WS) and cellulose (CEL), on the tensile properties and hardness of natural rubber was investigated. Additionally, resistance to thermo-oxidative aging and its effect on mechanical properties was assessed. Both WS and CEL significantly improve the mechanical properties of natural rubber, while also providing an environmentally friendly and economical alternative to traditional fillers.