Dynamic Fracture Toughness Behaviour of CFRP-Foam-CFRP Sandwich Composite and Particles Filled Hybrid Glass Fiber Cloth, Graphene Nanoplates Coated Glass Fiber Strand Composite Materials under Low Impact Velocity
Bibliographic record
Abstract
The main objective of the present study is to investigate the dynamic fracture toughness behaviors of CFRP-Foam-CFRP sandwich composite of V-notched through -thickness, surface, and un-notched specimens under Izod, and Charpy impact tests. The sandwich composite structures are made of cross-plied carbon fiber reinforced plastic (CFRP) composite faces with polyurethane foam core. CFRP composites are used to combine the upper face and the lower face through the core in stitched sandwich structures. Compressive strength of weight drop impact perforated and un-perforated sandwich composite specimens are measured from a universal testing machine. Also, particles (Al2O3, CNTs, and cement) filled glass fiber cloth and graphene nanoplates coated glass fiber strands reinforced polymer hybrid composite are fabricated for V-notched, un-notched Izod impact and Charpy impact tests. The results show that weight drop impact energy is lower than the Izod impact energy but higher than the Charpy impact energy, whereas the dynamic fracture toughness of Izod impact energy is more than the Charpy and weight drop impact energy due to geometry of impactor and sandwich specimen. However energy and dynamic fracture toughness of Al2O3, CNTs, and Cement filled un-notched hybrid composites higher than the notched hybrid composites under Izod Impact. The dynamic fracture toughness and energy of CNTs filled hybrid composites is higher than the sandwich composites, Al2O3, and Cement filled hybrid composites under Charpy Impact.
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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".