Global Symmetry, Sphericity, and Roundness in Hemisphere Forming of Fabric Reinforcements: Associations With Local Fiber Distortions and a Black‐Box Quality Prediction
Bibliographic record
Abstract
ABSTRACT An experimental analysis is conducted to identify the effect of design variables on a set of global geometrical quality metrics, including sphericity, roundness, and symmetry, in hemisphere draping of woven fabrics, next to the commonly employed primary (local) quality metrics concerning the fiber distortions. These secondary global metrics may be deemed important in practice, for example, during post‐forming processes and assembly of draped parts. Thirty punch‐forming configurations were designed and tested by varying process control factors, including (1) the fabric orientation, (2) the number of layers, (3) the number of clamps on the blank holder, and (4) the pressure distribution on the clamps. It was found that most samples with a fully uniform distribution of clamping pressure and sufficiently high mean pressure, result in a roundness value of 90% and above. Next to these control factors, a higher number of clamps (with equally‐spaced positions) generated a higher symmetry in the draped parts—identified via the center of mass displacement of samples. The sphericity for all tested configurations was similar ( ±4%; out of the maximum of 84% for a perfect hemisphere). The study also showed how a basic primary “quality score” as well as “forming quality classes” may be defined and predicted for design. Among the secondary metrics studied, the symmetry was most highly correlated with the primary fiber distortion factors, followed by the roundness.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".