Particle-based modeling of the compaction of fiber yarns and woven textiles
Bibliographic record
Abstract
This paper proposes a particle-based modeling method for predicting the constitutive behavior of textiles when subjected to various compressive loading conditions. The method, which is demonstrated for stacked layers of plain woven textiles, utilizes discrete mechanics as an alternative to traditional continuum mechanics. Fibers are modeled as a series of conjoined points, and their configurations are determined mechanistically using a modified Metropolis algorithm and inter-particle strain energy terms. The implementation presented in this paper enables intricate geometric modeling of textiles at microscopic, mesoscopic and macroscopic scales. It also enables extensive mechanical modeling of the textiles, from first principles, as they are loaded upon manufacturing of typical technical textile structures. While this paper focuses on the compaction behavior of weaves, the modeling method is readily adaptable to the analysis of shear, bending, buckling, punching, relaxation and other loading scenarios applied on a wide array of different textile types. These scenarios will be demonstrated in forthcoming publications. Comparative data from in silico and in situ testing shows excellent agreement. Results demonstrate an improvement in simulation accuracy over prior comparable modeling techniques. The method presented here successfully predicts the actual behavior of yarns, single-layer and double-layer textile stacks in compaction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".