Development of predictive models for elastomeric materials based on machine learning methods and analysis of the influence of formulation components

Abstract

The paper presents an approach to constructing predictive models for the physical and mechanical properties of elastomeric composites using machine learning methods. The relevance of the study is driven by the need to accelerate the development of new materials and reduce the labor intensity of full-scale experiments. An automated machine learning algorithm is proposed, encompassing stages of input data unification, feature space formation, and comparative analysis of regression models (Random Forest, Gradient Boosted Decision Trees, Gradient Tree-Boosting Tweedie, Poisson Regression, Light Gradient Boosting Machine и Stochastic Dual Coordinate Ascent). During experimental validation on datasets containing formulation data with varying content of sulfur, natural rubber (NR), and zinc oxide, predictions were made for theoretical density, Karrer plasticity, brittleness temperature, and curing temperature. It was established that ensemble methods demonstrate the highest predictive capability; however, model accuracy significantly depends on sample representativeness. Intervals of the studied parameters (particularly the 140–150 °C range for curing temperature) characterized by increased prediction uncertainty were identified, requiring additional algorithm calibration. The obtained results confirm the effectiveness of the proposed approach for formulation optimization and identification of hidden dependencies in the “composition-property” system.