Predicting Shrinkage in Polyester Reinforced by Glass Fabrics
Bibliographic record
Abstract
Polyester is one of the most common resins used in contact lay-up method because of its low cost, room-temperature curing, wide availability, ease of handing, etc. However, the main disadvantage of this resin is the large volumetric shrinkage after curing (up to about 0.5%). This represents a major problem because it can cause unexpected defects in the molded composite parts such as warpage, distortion, rippled surface, etc. The effect of resin shrinkage on composite deformations is very complex because of the anisotropic properties induced by the fibers, especially in woven fabric composites with interlacing yarns. Moreover, in many applications, when the part geometry has a double curvature, the forming process usually results in significant in-plane shear deformation of the interlaced yams. The angle between the fill and the warp threads is no longer orthogonal because the fabric must follow the shape of the mold. In this work, an approach to measure the shrinkage coefficients of the interlaced yarns of fabric structure has been developed. The recently proposed sub-plies model has been used to predict deformations due to resin shrinkage in woven fabric composite. Resin shrinkage can lead to an expansion in the laminates with specific angles between undulated yams, due probably to a straightening effect on the fibers. Expansions due to matrix shrinkage were verified on several woven laminates. Prediction of deformations due to matrix shrinkage by the sub-plies model is in good agreement with experimental 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".