Study on the properties of thermoplastic nonwoven interlayer toughened GF/EVE composites, i.e. toughening mechanism
Bibliographic record
Abstract
In order to improve the impact toughness and service life of GF/EVE composites, this paper applies the thermoplastic nonwoven fabric structure to the preparation of GF/EVE composites.The thermoplastic polyurethane was used as the raw material, and the meltblown method was used for the preparation of thermoplastic nonwoven fabrics, and then the prepared thermoplastic nonwoven fabrics were used for the preparation of GF/EVE composites through the VARTM device.For the properties of GF/EVE-TPU composites, specific test methods are given to define the moisture absorption rate and the erosion performance based on the consideration of the stress change of its hygrothermal properties, and the determination of the interlaminar fracture toughness is given.The critical damage threshold load of GF/EVE-TPU composites is 1.57kN, and its contact force increases with time, and the composites are aging in After 60 days, its moisture absorption and erosion weight loss in alkaline environment were 0.736% and 81.19%, respectively.The optimum fracture toughness value of 9g/m² thermoplastic nonwoven structure incorporated into GF/EVE composite was 0.97kJ/m², and the GIIC value of GF\EVE-TPU30 was increased compared with the GF/EVE material without interlaminar toughening by 183.83%.Combining the thermoplastic nonwoven fabric structure with GF/EVE composites can enhance the erosion resistance and interlaminar toughness of the composites and improve the service life of GF/EVE composites.
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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".