Influence of MWCNTs on the Mechanical Properties of Continuous Carbon Epoxy Composites
Bibliographic record
Abstract
The Carbon fiber reinforced epoxy composites finds wide range of applications in various fields such as aerospace, defence etc. The present study focuses on fabrication and mechanical characterization of different ply oriented Continuous Carbon Fibre reinforcedepoxy composites (CCFE). CCFE composites were fabricated using Drum winding, Hand Lay Up (HLU) followed by Compression Moulding with (0)4, (0/90/0/90)4, (45)4 orientations. Mechanical Characterisation was performed on the fabricated composites as per ASTM standards. CCFE with (0/90/0/90)4 orientation exhibited better compressive strength over other composites. 0.5,1.5 weight % of Multi Walled Carbon Nano-Tubes (MWCNTs) were reinforced in to the epoxy resin using ultrasonicator. The resin mixture with 0.5, 1.5 weight % of MWCNTs was used as matrix for the fabrication of (0/90/0/90)4 composites. Tensile, Compression, Flexural, Impact, Hardness and Water absorption tests were performed to investigate the effect of MWCNTs on (0/90/0/90)4 composites. CCFE samples with MWCNTs at 0.5% exhibited 1.63%, 5.23%, 16.47%, 3.9%, 29.6% increment in tensile strength, compressive strength, ILSS, impact strength, hardness, water absorptionand 36.9% decrement in flexural strength.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.000 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".