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<p>Modern industries, including aerospace, automotive, marine, construction, and defense increasingly need materials with a high strength-to-weight ratio, stiffness and dimensional stability. Recent interest has focused on replacing sustainable materials with substitutes for highly polluting composite components. Previous research faces challenges in developing sustainable composite materials that simultaneously meet the high demands for mechanical strength, wear resistance, thermal stability, and impact resistance. This research introduces a novel hybrid composite reinforced with coir and Kevlar fibers, enhanced with alumina (Al₂O₃) filler, and optimized using a deep learning model Modulated Deformable Convolution-based Graph Convolutional Network (MDCGCN) for precise mechanical performance prediction. The innovative approach integrates sustainable materials with advanced computational modeling to achieve superior strength, wear resistance, and thermal stability, eco-friendly and high-performance composites in engineering applications. Coir fiber enhances sustainability and cost-effectiveness, while Kevlar provides high strength, resulting in a balanced, eco-friendly hybrid composite incorporated with epoxy, a bonding and chemical resistance agent. To improve the composite’s mechanical and thermal properties, alumina ceramic fillers (up to 10 wt%) are incorporated into the epoxy matrix because of their high stiffness and hardness. The effects of fiber orientation (0°, 45°, 90°), fiber volume fraction (10–40%), and alumina content (0–10 wt%) are analyzed using response surface methodology and analysis of variance. In accordance with the American Society for Testing and Materials (ASTM) standard, the tensile strength, flexural strength, compressive strength, impact resistance, and surface hardness properties are evaluated. Outcomes demonstrated outstanding performance with the optimal combination of 40% fiber volume fraction, 10% alumina filler content, at 90<sup>0</sup> orientations, achieving a higher tensile strength of 127&#xa0;MPa, flexural strength of 148.4&#xa0;MPa, impact resistance of 34.3&#xa0;kJ/m², and surface hardness of 83.8&#xa0;N/mm<sup>2</sup> in comparison to existing natural fiber composites.</p>

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Multi-factor optimization of epoxy based Kevlar-coir hybrid composites with alumina using deep graph learning

  • 期刊论文

  • 发表日期：

    2026年3月23日

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  • Vangapandu Chandrakala,
  • Sridevi Gudepu,
  • Reddipalli Aditya,
  • Ratnala Prasad,
  • P. Sri Gowri Padmaja

摘要

Modern industries, including aerospace, automotive, marine, construction, and defense increasingly need materials with a high strength-to-weight ratio, stiffness and dimensional stability. Recent interest has focused on replacing sustainable materials with substitutes for highly polluting composite components. Previous research faces challenges in developing sustainable composite materials that simultaneously meet the high demands for mechanical strength, wear resistance, thermal stability, and impact resistance. This research introduces a novel hybrid composite reinforced with coir and Kevlar fibers, enhanced with alumina (Al₂O₃) filler, and optimized using a deep learning model Modulated Deformable Convolution-based Graph Convolutional Network (MDCGCN) for precise mechanical performance prediction. The innovative approach integrates sustainable materials with advanced computational modeling to achieve superior strength, wear resistance, and thermal stability, eco-friendly and high-performance composites in engineering applications. Coir fiber enhances sustainability and cost-effectiveness, while Kevlar provides high strength, resulting in a balanced, eco-friendly hybrid composite incorporated with epoxy, a bonding and chemical resistance agent. To improve the composite’s mechanical and thermal properties, alumina ceramic fillers (up to 10 wt%) are incorporated into the epoxy matrix because of their high stiffness and hardness. The effects of fiber orientation (0°, 45°, 90°), fiber volume fraction (10–40%), and alumina content (0–10 wt%) are analyzed using response surface methodology and analysis of variance. In accordance with the American Society for Testing and Materials (ASTM) standard, the tensile strength, flexural strength, compressive strength, impact resistance, and surface hardness properties are evaluated. Outcomes demonstrated outstanding performance with the optimal combination of 40% fiber volume fraction, 10% alumina filler content, at 900 orientations, achieving a higher tensile strength of 127 MPa, flexural strength of 148.4 MPa, impact resistance of 34.3 kJ/m², and surface hardness of 83.8 N/mm2 in comparison to existing natural fiber composites.