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<p>Dried leaves are a highly regarded renewable resource and the primary source of cellulosic plant material. It is rumored that dried leaf fibers may enhance the strength of polymer laminates comparably to synthetic fibers. This study is distinctive as it used artificial neural network (ANN) methodology to investigate the influence of dried leaves fiber, alumina, copper, and silicon carbide reinforcement on the thermal, physical, and mechanical properties of epoxy, polylactic acid, and vinyl-ester polymers. The wet layup procedure supported by an ultrasonication bath was employed to fabricate these composites under ambient circumstances. The findings indicate that the dried leaves-alumina fillers enhanced the mechanical and thermal stability of all three polymers compared to the other samples. The Fourier-transform infrared (FTIR) spectra indicate that the fillers within the matrix form robust interfacial bonds, possibly due to the generation of novel hydroxyl functional groups. The thermogravimetric analysis indicated that the hybrid composites composed of dried leaves, silicon carbide filler, and polymer exhibited superior thermal stability. The findings were statistically significant at the 95% confidence level, as determined by the one-way ANOVA analysis. This study supports the notion that investigating innovative polymer composites is essential, and it demonstrates the efficacy of employing advanced modeling to accurately anticipate material properties.</p>

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ANN based evaluation of thermal, mechanical, and physical properties of dried leaf fiber reinforced hybrid polymer composites

  • 期刊论文

  • 发表日期：

    2026年5月6日

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  • Vitthal Sadashiv Gutte,
  • Mayuri H. Molawade,
  • Satpalsing Devising Rajput,
  • Sneha Sudhakar Satpute,
  • Sanober Sultana Shaikh,
  • Poonam Bhosale

摘要

Dried leaves are a highly regarded renewable resource and the primary source of cellulosic plant material. It is rumored that dried leaf fibers may enhance the strength of polymer laminates comparably to synthetic fibers. This study is distinctive as it used artificial neural network (ANN) methodology to investigate the influence of dried leaves fiber, alumina, copper, and silicon carbide reinforcement on the thermal, physical, and mechanical properties of epoxy, polylactic acid, and vinyl-ester polymers. The wet layup procedure supported by an ultrasonication bath was employed to fabricate these composites under ambient circumstances. The findings indicate that the dried leaves-alumina fillers enhanced the mechanical and thermal stability of all three polymers compared to the other samples. The Fourier-transform infrared (FTIR) spectra indicate that the fillers within the matrix form robust interfacial bonds, possibly due to the generation of novel hydroxyl functional groups. The thermogravimetric analysis indicated that the hybrid composites composed of dried leaves, silicon carbide filler, and polymer exhibited superior thermal stability. The findings were statistically significant at the 95% confidence level, as determined by the one-way ANOVA analysis. This study supports the notion that investigating innovative polymer composites is essential, and it demonstrates the efficacy of employing advanced modeling to accurately anticipate material properties.