Thermal cycling of composite laminates made of out-of-autoclave materials
Bibliographic record
Abstract
Abstract Carbon fiber-reinforced polymer material has been widely used in space/aerospace industries for manufacturing of spacecraft structures, satellite panels and antennas. In space, composites can be subjected to periodic thermal cycling (TC) in which temperature ranges from −196°C to 180°C, depending on the operational condition. The effect of TC on the properties of flat laminates made of unidirectional (UD) and fabric out-of-autoclave (OOA) material will be presented. Flat laminates were made using the recommended cure cycle by the material supplier. Then, the samples were cut and subjected to the thermal cycle. To do so, the samples were dipped into liquid nitrogen (−196°C) and then transferred to the oven (140°C). After different numbers of cycles (30, 60, 100, 150 and 200), the cross section of the specimens was examined under a microscope for microcrack detection. Other mechanical and physical properties including interlaminar shear strength, storage modulus and coefficient of thermal expansion (CTE) were measured. It was identified that TC can affect the examined properties by two competing factors: (i) more cross-linking in the polymer chain due to post-curing at high temperature during TC and (ii) microcrack formation due to the induced thermal stresses as a result of the matrix/fiber CTE mismatch. It was found that TC can cause microcrack formation and propagation around the voids in the laminate and affect its properties. Depending on the size and shape of the void, microcracks can form at different stages of TC.
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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".