Dynamic Impact Properties of Carbon-Fiber-Reinforced Phenolic Composites Containing Microfillers
Bibliographic record
Abstract
The addition of nano- and microfillers to carbon-fiber-reinforced polymers (CFRPs) to improve their static mechanical properties is attracting growing research interest because their introduction does not increase the weight of parts made from CFRPs. However, the current understanding of the high strain rate deformation behaviour of CFRPs containing nanofillers/microfillers is limited. The present study investigated the dynamic impact properties of carbon-fiber-reinforced phenolic composites (CFRPCs) modified with microfillers. The CFRPCs were fabricated using 2D woven carbon fibers, two phenolic resole resins (HRJ-15881 and SP-6877), and two microfillers (colloidal silica and silicon carbide (SiC)). The amount of microfillers incorporated into the CFRPCs varied from 0.0 wt.% to 2.0 wt.%. A split-Hopkinson pressure bar (SHPB), operated at momentums of 15 kg m/s and 28 kg m/s, was used to determine the impact properties of the composites. The evolution of damage in the impacted specimens was studied using optical stereomicroscope and scanning electron microscope. It was found that, at an impact momentum of 15 kg m/s, the impact properties of HRJ-15881-based CFRPCs increased with SiC addition up to 1.5 wt.%, while those of SP-6877-based composites increased only up to 0.5 wt.%. At 28 kg m/s, the impact properties of the composites increased up to 0.5 wt.% SiC addition for both SP-6877 and HRJ-15881 based composites. However, the addition of colloidal silica did not improve the dynamic impact properties of composites based on both phenolic resins at both impact momentums. The improvement in the impact properties of composites made with SiC microfiller can be attributed to improvement in crystallinity offered by the α-SiC type microfiller used in this study. No fracture was observed in specimens impacted at an impact momentum of 15 kg m/s. However, at 28 kg m/s, edge chip-off and cracks extending through the surface were observed at lower microfiller addition (≤1 wt.%), which became more pronounced at higher microfiller loading (≥1.5 wt.%).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".