Finite Element Modeling of Woven Fabric Composites at Meso-Level under Combined Loading Modes
Bibliographic record
Abstract
Woven fabrics are among the most important materials used in today's modern industries. Next to their high mechanical properties, they are easy to handle in the dry or preimpregnated pre-forms, offer good drape-ability and are particularly suited for manufacturing of doubly curved components, membranes, inflatable structures, etc In the dry form, fabrics can be formed into a variety of threedimensional (3D) shapes and then consolidated with resin via resin transfer molding (RTM) or other manufacturing processes Reliable models capable of predicting the mechanical behaviour of woven fabric materials are not fully developed yet. The biggest challenge in this regard is perhaps the multi-scale nature of the fabric materials. Dry fabrics at macro level are composed of numerous yarns interlaced into each other. The yarns usually have characteristic length in the scale of millimetres and their interaction and behaviour at the fabric level can greatly influence the macro-level material behaviour Yarns themselves are heterogeneous media made of bundles of very thin and long fibers. Figure
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".