4.4 Article

The Strain Rate Effect on the Compression Properties of Basalt/Carbon Fiber Reinforced Composites

Journal

APPLIED COMPOSITE MATERIALS
Volume 29, Issue 3, Pages 1007-1020

Publisher

SPRINGER
DOI: 10.1007/s10443-021-09997-6

Keywords

Composites; Fibers; Impact resistance; Strength; Strain rate

Funding

  1. National Natural Science Foundation of China [12002169]
  2. Natural Science Foundation of Jiangsu Province of China [BK20170837]
  3. Postgraduate Research & Practice Innovation Program of Jiangsu Province of China [KYCX21_0342]

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The effect of adding a basalt fiber layer to carbon-fiber-reinforced plastic was studied, and the stress-strain relationship was obtained through experiments. The results showed that the failure strain of the material with the added basalt fiber layer was 45% higher than that of the carbon-fiber-reinforced plastic, and it was dependent on the strain rate.
Adding a basalt fiber layer (BF) to carbon-Fiber-reinforced plastic (CFRP) material can effectively improve its extensibility. In the work described in this study, a study of hybrid layup BF and CFRP (BCFRP) compressive performance and strain-rate effect was conducted. Through quasistatic loading experiment and split Hopkinson pressure bar experiments, the stress-strain relationship of BCFRP specimens at different strain rates was obtained, and the rate-dependent constitutive equation with only average strain-rate parameters. Depending on the form of failure, the range of average strain rates to which this constitutive equation applies can be determined. Results show that the failure strain of BCFRP under quasistatic conditions is 45% higher than that of CFRP; its failure mode is related to the strain rate. As the average strain rate increases, the main failure mode of the specimen changes from delamination and matrix fracture to fiber fracture failure. The strain rate-dependent constitutive equation has a high degree of fitting to the experimental results, and the single-parameter rate-dependent constitutive equation has good prediction accuracy for the experimental results, with the maximum error being 6.13%.

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