An elaboration on the shear characterization of dry woven fabrics using trellising tests
Bibliographic record
Abstract
Abstract Material characterization of woven fabric composites in dry form is one of the most crucial steps prior to the design and optimization of composite manufacturing processes. High stiffness of yarns under axial tension and low stiffness under in‐plane shear makes the latter the dominant deformation mode during draping of woven fabrics. Bias‐extension (BE) and shear frame (SF) tests are two widely used experimental setups for the characterization of woven fabrics under the shear (also called trellising) mode. This article outlines two general approaches for the characterization and normalization of data collected from the above standard tests. The first approach is based on the total energy absorbed by the fabric specimen along with the total work induced by the external force on the moving head of the tensile test machine. The second approach uses a variational formulation along with the principle of virtual work. Using both approaches, it is shown how, in the BE test, an auxiliary specimen with a different aspect ratio can be used to cope with the problem of nonuniform deformation (formation of three different shear regions) in the specimens. To illustrate the application of both methods, they are applied to predict in‐plane shear stiffness of a glass/PP plain weave under SF and BE tests. It is suggested that the shear stress can be used as a normalized parameter to compare data from different trellising tests. POLYM. COMPOS., 2013. © 2013 Society of Plastics Engineers
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".