Experimental and numerical investigation on the open-hole compressive strength of AFP composites containing gaps and overlaps
Bibliographic record
Abstract
Laminated composite structures manufactured via the automated fiber placement process inherently contain process defects know as gaps and overlaps. These defects raise concerns when they are located on or near holes intended for mechanical fastening. This investigation attempts to predict the effect of automated fiber placement-generated defects on the open-hole compression strength by combining both experimental tests and numerical simulation. Tested open-hole compression specimens containing gaps and overlaps oriented at 0° or 90° and centered on or shifted near the hole show that, depending on their location, the gaps and overlaps may have negative, negligible, or positive effects on the open-hole compression strength. The better than expected effects are compatible with microscopic observations that clearly show the rearrangement of the plies during the consolidation process, which prevent the formation of deleterious discontinuities. Incorporating these observations in a numerical model, which simulates gaps and overlaps embedded inside the composite laminates, and applying a progressive failure analysis, confirms that the effects of automated fiber placement defects depend as much on their type as on their location relative to the hole center. Finally, the results obtained from a parametric study provided further explanation on the effects of automated fiber placement defects on the failure strength of perforated composite laminates.
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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 | 0.001 |
| 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 | 0.000 | 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".