In‐plane anisotropic permeability characterization of deformed woven fabrics by unidirectional injection. Part II: Prediction model and numerical simulations
Bibliographic record
Abstract
Abstract The production of polymer composites by resin injection has strongly increased in the recent years for a wide variety of industrial applications. Manufacturing techniques are continuously optimized for higher and faster production cycles and high performance composites are needed to meet industrial requirements. Actually, the reduction of cost and cycle time is the main motivation for liquid composite molding (LCM) process simulation. In this context, the characterization of preform permeability is a key issue in numerical flow analysis. This investigation concerns the experimental study and development of a predictive model for deformed fabric permeability. Such deformations occur when a fabric is draped on a complex surface. A local shear appears. In Part I, an experimental procedure has been described to measure the permeability of deformed fabrics, and a new methodology presented to characterize the in‐plane permeability tensor. In order to implement experimental results in numerical simulation, a permeability model for deformed fabrics is required. Part II develops a predictive model of the principal permeabilities of deformed fabrics and of the orientation of the permeability tensor. Based on unsheared fabric measurements, the model takes into account the unit cell deformation during shear and the initial elliptic flow pattern orientation and anisotropy ratio of undeformed fabrics. Model predictions are corroborated with sheared fabric measurements. Finally, numerical simulations for an automotive body part are carried out to illustrate the effects of fabric shearing on the filling of the composite part. POLYM. COMPOS. 28:812–827, 2007. © 2007 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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".