On the Effect of Uncertainty Factors on Mechanical Behavior of Woven Fabric Composites at Meso-Level
Bibliographic record
Abstract
Unit cell modeling of woven fabric composites at meso-level has been advantageous in finding equivalent mechanical properties of different weave architectures without performing physical experiments on each new fabric. The obtained properties, in turn, can be used in the macro-level modeling and simulation of large composite structures. Models used for this purpose, however, often consider a perfect description of unit cells, while in practice fabrics are not always fabricated under ideal conditions and flaws like fiber misalignment, material and/or geometrical defects are present. A benchmark work covering effects of this kind on the mesoscopic behavior of woven fabrics is underway. The aim of this paper is to present a statistical way to approach the problem by studying the main effects of such uncertainty/noise factors along with their levels of significance. Namely, a one-factor-at-a-time screening method is selected to identify the effect of (1) fiber misalignment, (2) fiber modulus variation, (3) geometrical flaws in yarn section, (4) unpredictable friction between weft and warp yarns. Computer experiments are done using FE modeling of a plain weave unit cell under the uniaxial, equibiaxial, and trellising (shear) modes. A parameter sensitivity analysis is conducted to identify the most significant factors and the extent to which each can independently contribute to the variation of load-displacement curves (i.e., testing data non-repeatabilities).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".