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<p>In the preparation of hybrid fiber-reinforced composites, optimizing fiber blend ratios requires extensive experimental iterations, complicating material design and quality control processes. In this study, regression models were established based on experimental results to evaluate the effects of variables and their interactions on composite porosity, mechanical properties, and fracture strength. The results revealed that employing single factor and two-factor three-level response surface methodology (RSM) effectively enhanced testing efficiency while achieving breakthroughs in data reliability. Analysis of variance (ANOVA) revealed that glass fibers exerted a greater influence on the composite’s porosity and flexural strength, while polyvinyl alcohol (PVA) fibers significantly impacted the fracture toughness. Furthermore, glass and PVA fibers demonstrated strong synergistic effects in compressive strength tests. The difference in elastic modulus between the fibers, which induced the formation of a gradient stress field in the matrix, reduced stress concentration and effectively prevented crack initiation and propagation. The multi-objective optimization results of the model exhibited a small relative error compared to the experimental data obtained from validation tests, demonstrating the reliability of the RSM optimization process. The final data indicated that the solid waste-based composite material exhibited excellent mechanical properties when the optimal glass and PVA fiber contents were 0.342 vol% and 0.600 vol%, respectively.</p>

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Synergistic optimization of glass/PVA hybrid fiber-reinforced solid waste matrix composites: mechanical enhancement and crack mitigation via response surface methodology

  • 期刊论文

  • 发表日期：

    2026年1月27日

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  • Duoming Wang,
  • Yanxin Chen,
  • Chang Chen,
  • Bo Zhao,
  • Zengyuan Tian

摘要

In the preparation of hybrid fiber-reinforced composites, optimizing fiber blend ratios requires extensive experimental iterations, complicating material design and quality control processes. In this study, regression models were established based on experimental results to evaluate the effects of variables and their interactions on composite porosity, mechanical properties, and fracture strength. The results revealed that employing single factor and two-factor three-level response surface methodology (RSM) effectively enhanced testing efficiency while achieving breakthroughs in data reliability. Analysis of variance (ANOVA) revealed that glass fibers exerted a greater influence on the composite’s porosity and flexural strength, while polyvinyl alcohol (PVA) fibers significantly impacted the fracture toughness. Furthermore, glass and PVA fibers demonstrated strong synergistic effects in compressive strength tests. The difference in elastic modulus between the fibers, which induced the formation of a gradient stress field in the matrix, reduced stress concentration and effectively prevented crack initiation and propagation. The multi-objective optimization results of the model exhibited a small relative error compared to the experimental data obtained from validation tests, demonstrating the reliability of the RSM optimization process. The final data indicated that the solid waste-based composite material exhibited excellent mechanical properties when the optimal glass and PVA fiber contents were 0.342 vol% and 0.600 vol%, respectively.