Finite Element Modeling of Woven Fabric Composites at Meso-Level under Combined Loading Modes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
IntroductionWoven 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 (Cavallaro et al., 2003; 2007).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 (Boisse et al., 2007).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 (Guagliano and Riva, 2001).Yarns themselves are heterogeneous media made of bundles of very thin and long fibers.Figure 1 shows different hierarchical levels in a woven fabric along with their typical dimensions. Micro-level fibers ~10 -6 m Macro-level part ~10 0 m Meso-level yarns ~10 -3 m Fig. 1. 3 Hierarchical levels in woven fabrics www.intechopen.com
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it