Characterization of anisotropic permeability from flow front angle measurements
Bibliographic record
Abstract
Textile permeability is a generally anisotropic material property, which characterizes the ease of establishing a resin flow through the fibrous reinforcement in Liquid composite molding (LCM) processes. Unidirectional injection experiments are commonly performed to determine in‐plane permeability. Effective permeability values have to be measured along three different textile directions to calculate the full in‐plane permeability tensor. This article presents a strategy to reduce the number of the required unidirectional experiments to two or even one by considering the angle that the flow front forms with the measurement direction. The relationship between this flow front angle and the permeability tensor elements was derived theoretically and verified by both simulations and experiments with various textile reinforcements. In addition, two methods were investigated to measure the flow front angle and the effective permeability during the experiments: a standard approach based on visual observations and a new method that relies on three pressure sensors, applicable also in the case of nontransparent tooling. The results show that: (I) the two methods provide consistent measurements and are substantially equivalent; (II) the strategy devised to characterize permeability by measuring the flow front angle is effective and accurate; (III) the proposed procedure allows reducing considerably the time and the material samples required for permeability characterization by unidirectional experiments. POLYM. COMPOS., 37:2037–2052, 2016. © 2015 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".