Machine Learning-Driven Prediction of Composite Materials Properties Based on Experimental Testing Data.

Abstract

The growing demand for high-performance and cost-effective composite materials necessitates advanced computational approaches for optimizing their composition and properties. This study aimed at the application of machine learning for the prediction and optimization of the functional properties of composites based on a thermoplastic matrix with various fillers (two types of fibrous, four types of dispersed, and two types of nano-dispersed fillers). The experimental methods involved material production through powder metallurgy, further microstructural analysis, and mechanical and tribological testing. The microstructural analysis revealed distinct structural modifications and interfacial interactions influencing their functional properties. The key findings indicate that optimal filler selection can significantly enhance wear resistance while maintaining adequate mechanical strength. Carbon fibers at 20 wt. % significantly improved wear resistance (by 17-25 times) while reducing tensile strength and elongation. Basalt fibers at 10 wt. % provided an effective balance between reinforcement and wear resistance (by 11-16 times). Kaolin at 2 wt. % greatly enhanced wear resistance (by 45-57 times) with moderate strength reduction. Coke at 20 wt. % maximized wear resistance (by 9-15 times) while maintaining acceptable mechanical properties. Graphite at 10 wt. % ensured a balance between strength and wear, as higher concentrations drastically decreased mechanical properties. Sodium chloride at 5 wt. % offered moderate wear resistance improvement (by 3-4 times) with minimal impact on strength. Titanium dioxide at 3 wt. % enhanced wear resistance (by 11-12.5 times) while slightly reducing tensile strength. Ultra-dispersed PTFE at 1 wt. % optimized both strength and wear properties. The work analyzed in detail the effect of PTFE content and filler content on composite properties based on machine learning-driven prediction. Regression models demonstrated high R-squared values (0.74 for density, 0.67 for tensile strength, 0.80 for relative elongation, and 0.79 for wear intensity), explaining up to 80% of the variability in composite properties. Despite its efficiency, the limitations include potential multicollinearity, a lack of consideration of external factors, and the need for further validation under real-world conditions. Thus, the machine learning approach reduces the need for extensive experimental testing, minimizing material waste and production costs, contributing to SDG 9. This study highlights the potential use of machine learning in polymer composite design, offering a data-driven framework for the rational choice of fillers, thereby contributing to sustainable industrial practices.