Machine Learning Models for Strength Prediction of Carbon Fiber Reinforced Polymers - Berladir , Khrystyna; Sokolov, Oleksandr; Hatala, Michal; Denysenko, Yuliia; Ivanov, Vitalii (Management and Production Engineering Review, 2026)
This paper applied and benchmarked machine learning regression algorithms for predicting the tensile strength of carbon fiber-reinforced plastics. A comprehensive statistical analysis revealed significant linear correlations between the input parameters (ratio of matrix and filler concentration, composite density) and the target variable (tensile strength). The dataset consisted of 150 experimental data points. However, nonlinear models significantly outperformed linear models in terms of prediction accuracy. The highest accuracy on an independent test dataset was demonstrated by the neural network model (R² = 0.996351), as well as the Gaussian process regression models (R² = 0.996246) and the ensemble method (R² = 0.996096). Classical linear regression yielded a significantly lower result (R2 = 0.89498), confirming that the overall dependence is a significant, nonlinear, multidimensional function. The key result is the detection of nonlinear behavior in the «polymer−fiber» system: the tensile strength reaches a maximum in the optimal reinforcement phase (10–20 wt.%) and then decreases sharply due to fiber agglomeration and porosity. The success of nonlinear ML models is based on their ability to effectively model these complex, hidden dependencies, paving the way for accelerated materials design.