Shear response of woven fabric composites under meso-level uncertainties
Bibliographic record
Abstract
Experimental results in the literature indicate notable non-repeatabilities during mechanical testing of woven fabrics, even under the same loading conditions, which may be linked to the presence of defects and uncertainties at meso or micro levels in yarns. Sources of such uncertainties can include both yarn dimensional tolerances and variations in the fiber/matrix material properties. The aim of this article is to conduct a systematic sensitivity analysis on the meso-level uncertainty factors in a typical woven fabric and identify the most significant factors and their interactions under a trellising mode. A finite element model capable of capturing behavior of dry yarns, along with a two-level full-factorial design approach has been employed. Factorial designs are split into two categories of the geometrical and material factors. It is shown how the obtained range of variations from the above statistical designs may be used to capture non-repeatability in some trellising tests.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".