Effect Of The Interlacing Pattern On The Compaction Behaviour Of 3D Carbon Fibre Textile Reinforcements
Bibliographic record
Abstract
3D reinforcements were introduced to mitigate and overcome limitations arising from the use of traditional textile reinforcements in the construction of polymer matrix composites (PMC), in terms of their resistance to impact and inter-laminar shear strength (ILSS), both resulting from delamination.Many thick interlaced textile structures were developed towards that end but many more possibilities exist in terms of yarn interlacing patterns, leading to different types of 3D textiles that may potentially be built.The effect of different interlacing patterns on the compaction behaviour of such interlaced 3D textiles is probed in this paper.Trends and differences in the compaction behaviour were observed for 5 series of 3 consecutive compaction cycles applied to 16-layers interlaced 3D reinforcements.Those trends and differences are presented and discussed.Overall, the maximum recorded volume fraction ranged from 0.58 to 0.86.Values of the volume fraction reached in the first cycle C 1 were always lower than in following cycles C 2 and C 3 .Although some elements of behaviour were common to all samples and tests, differences also emerged between samples featuring different interlacing patterns.
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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 | 0.001 |
| 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".