Concave and convex small radius bending behavior of single and multiple layers reinforcement fabrics
Bibliographic record
Abstract
Multilayer reinforcement fabrics are increasingly used for manufacturing structural polymer composites. In liquid molding processes, dry reinforcement fabrics are draped on a mold first, and infused with a liquid resin such as an epoxy in a subsequent manufacturing step. This presents major advantages in terms of operational flexibility and costs. However, draping multilayer reinforcement fabrics on complex mold geometries is challenging. Small radius mold corners constitute a major manufacturing challenge as they lead to variability in dry fabric positioning and resin-rich corners in polymer composite parts. Spring-back of fabrics bent over or into single curvature mold corners is a widespread industrial concern. However, contrary to draping over double-curvature surfaces, bending spring-back from convex or concave single-curvature corners has received very limited attention. No testing method is available. This paper quantifies reinforcement fabric bending spring-back. Single and multiple layer stacks were bent along three directions over convex and into concave 90° corners with five radii spanning 1.59 mm to 12.70 mm. In all cases, five replicated tests enabled variability quantification. Fabric stacks were also quantified using cantilevered bending tests for comparison purposes. Mold radius was found to affect the behavior to a larger extent than testing direction, number of layers or use of a binder.
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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) | 0.001 | 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".