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<p>Mechanical performance and synergistic effects of polymer composites reinforced with short carbon fibers (SCF) and cenospheres, were optimized in strength, stiffness, and impact resistance for structural applications. Among the fabricated formulations, the Cr15C5 composite (15% SCF, 5% cenosphere) demonstrated superior properties, with flexural, impact, and tensile strengths enhanced by 136.6%, 402% and 97.8%, respectively, compared to the unreinforced control. Composites with single reinforcements also showed improvements: Cr0C20 (cenospheres only) exhibited 20.6% higher flexural strength and 8.2% higher impact strength, while Cr20C0 (SCF only) achieved a 61.1% increase in tensile strength. Microscopic analysis revealed that SCF promotes effective load transfer and crack deflection, whereas cenospheres enhance energy absorption, together imparting balanced performance. To explore data-driven approaches, a Random Forest Regressor was applied to establish composition–property correlations. The model provided moderate accuracy for tensile strength prediction (RMSE = 6.61&#xa0;MPa; R² = -0.83), larger deviations for flexural strength (RMSE = 21.99&#xa0;MPa; R² = -1.22), and comparatively better agreement for impact strength (RMSE = 1.36&#xa0;kJ/m²; R² = 0.47), reflecting the limitations imposed by the relatively small dataset. Overall, the findings highlight the potential of SCF-cenosphere hybrid reinforcements in producing lightweight, high-strength composites for automotive and aerospace applications, while demonstrating the feasibility and current constraints of machine learning–based property prediction in composite design.</p> Graphical abstract <p></p>

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Machine learning validation of mechanical performance in short carbon fibers-cenosphere reinforced polymer composites

  • 期刊论文

  • 发表日期：

    2026年3月9日

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  • Vishwas Mahesh,
  • Vinyas Mahesh,
  • Sharnappa Joladarashi,
  • B. R. Abdul Razik,
  • R. S. Akshatha,
  • N. S. Hamsa,
  • R. Sourav

摘要

Mechanical performance and synergistic effects of polymer composites reinforced with short carbon fibers (SCF) and cenospheres, were optimized in strength, stiffness, and impact resistance for structural applications. Among the fabricated formulations, the Cr15C5 composite (15% SCF, 5% cenosphere) demonstrated superior properties, with flexural, impact, and tensile strengths enhanced by 136.6%, 402% and 97.8%, respectively, compared to the unreinforced control. Composites with single reinforcements also showed improvements: Cr0C20 (cenospheres only) exhibited 20.6% higher flexural strength and 8.2% higher impact strength, while Cr20C0 (SCF only) achieved a 61.1% increase in tensile strength. Microscopic analysis revealed that SCF promotes effective load transfer and crack deflection, whereas cenospheres enhance energy absorption, together imparting balanced performance. To explore data-driven approaches, a Random Forest Regressor was applied to establish composition–property correlations. The model provided moderate accuracy for tensile strength prediction (RMSE = 6.61 MPa; R² = -0.83), larger deviations for flexural strength (RMSE = 21.99 MPa; R² = -1.22), and comparatively better agreement for impact strength (RMSE = 1.36 kJ/m²; R² = 0.47), reflecting the limitations imposed by the relatively small dataset. Overall, the findings highlight the potential of SCF-cenosphere hybrid reinforcements in producing lightweight, high-strength composites for automotive and aerospace applications, while demonstrating the feasibility and current constraints of machine learning–based property prediction in composite design.

Graphical abstract