A coupled non-orthogonal hypoelastic constitutive model for woven fabrics
Bibliographic record
Abstract
Wrinkling, a common manufacturing-induced defect, can be delayed in dry woven fabrics by applying tension during processing. Such a known solution in industrial practice, is still not sufficiently implemented in the modeling and simulation of woven fabrics. This study proposes a new constitutive model considering fabric’s inherent coupling, non-orthogonality, and non-linearity. First, the intrinsic coupling in question is distinguished from the typical coupling presented by the Hook’s law. Then, the most influential forms of coupling are chosen and introduced to the model. Moreover, via experimental evidence, we reveal that the concept of inherent coupling raises a new issue: the load history dependency of the fabric. Accordingly, the base model was evolved to a hypoelastic form to capture this dependency. The stiffness functions of the introduced model are determined, considering the mechanical behavior of a typical polypropylene (PP)/glass plain weave and the underlying meso-scale sources of the coupling. An inverse method was employed to identify the unknown model parameters. While the basic loading modes such as the uniaxial tension and picture frame tests are utilized for model calibration, the more complex loading conditions such as the simultaneous biaxial tensile-shear test, which would be more comparable to actual forming processes, are selected as the independent datasets to validate accuracy of the model. Comparisons with the experimental results demonstrated that the coupled model predicts the behavior of the fabric more accurately than the uncoupled model.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".