Predictive Modelling and Optimisation of Rubber Blend Mixing Using a General Regression Neural Network

作者：Ivan Kopal, Ivan Labaj, Júliana Vršková, Marta Harničárová, Jan Valíček, Alžbeta Bakošová, Hakan Tozan, Ashish Khanna · 发表于：Polymers · 年份：2025 · DOI：10.3390/polym17131868 · 被引用次数：7 · 研究领域：Manufacturing Process and Optimization、Mineral Processing and Grinding、Metallurgy and Material Forming

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.