In-plane anisotropic permeability characterization of deformed woven fabrics by unidirectional injection
Bibliographic record
Abstract
SUMMARY: Because of the increasing use of polymer composites in a wide variety of industrial applications, the manufacturing of complex composite parts has become an important research topic. When a part is manufactured by liquid composite moulding (LCM), the reinforcement undergoes a certain amount of deformation after closure and sealing of the mould. In the case of bidirectional woven fabrics, this deformation may significantly affect the resin flow and mould filling because of changes in the values of permeability. Among other considerations that govern the accuracy of numerical simulations of mould filling, it is important to predict the changes of permeability as a function of the local shearing angle of the preform. The resin flow through a fibrous reinforcement is governed by Darcy’s law, which states that the fluid flow rate is proportional to the pressure gradient. The shape of the flow front in a pointwise injection through an anisotropic preform is an ellipse. This article describes a new methodology based on the ellipse equation to derive the in-plane permeability tensor from unidirectional injection experiments in deformed woven fabrics. It also presents a mathematical model that predicts the principal permeabilities and their orientation for sheared fabrics from the permeability characterization of unsheared fabrics. Unidirectional flow experiments were conducted for a non-stitched, balanced, woven fabric for different shearing angles and fiber volume fractions. Experimental results are presented for deformed and undeformed fabrics obtained by unidirectional flow measurements as well as a comparison of the proposed characterization methodology with radial flow experiments. As result of this study, a new computer interface was developed allowing real-time acquisition data and processing for both unidirectional and transverse permeability measurements.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".