Thermal cycle effects on laminated composite plates containing voids
Bibliographic record
Abstract
Composite structures are often cured in an autoclave to acquire the required space grade quality. Now the industry is focusing on the out of autoclave manufacturing method which leads to more voids inside laminate with respect to those manufactured in the autoclave. In the present work, the influence of voids on microcrack formation under thermal cycling and environmental conditions was analyzed. Thermal cycle experiments were performed using liquid nitrogen and oven, followed by microscopic observation of the polished cross-section of the 90° layered plies. Cracks were monitored, counted, and measured with respect to void and void free areas. Void content was characterized using microscopic and ImageJ software was used. It was observed that the microcracks will be formed both around the voids and in void free areas. As the number of thermal cycle increases, the number of microcrack around the voids increases much faster than compared to the void free areas. Also it was observed that most of microcracks were propagated in the transverse direction. Interlaminar shear strength was measured. Results indicate that interlaminar shear strength reduces as the number of cycle rises due to the increase in the microcrack density. Finite element method was used to simulate the process. The micro, meso, and macro model were created with respect to original samples voids and positions to calculate the stress distribution and its concentration. Good agreement between experiment and simulation was observed.
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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 | 0.001 |
| 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 | 0.000 |
| 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".