Effect of autoclave process on the quality of thermoplastic composite truncated cones manufactured using automated fiber placement technique
Bibliographic record
Abstract
Abstract In order to investigate the effect of autoclave process on the stiffness properties of thermoplastic composite truncated cones made by automated fiber placement technique (AFP), two short truncated cones were made out of advanced thermoplastic composite material (carbon fiber/PEEK). Then, the truncated cones were tested by impulse excitation of vibration to get experimental natural frequencies. Tensile and compression test coupons were made out of the same material using hand layup and autoclave process, and stiffness properties were characterized experimentally. Furthermore, finite element analysis was performed to extract theoretical natural frequencies using material properties obtained from coupon testing. Comparison between the natural frequencies obtained using the finite element method and experimental modal analysis was conducted. Furthermore, the two truncated cones were treated inside the autoclave, and changes in the thickness and density were measured. After autoclave treatment, experimental modal analysis was repeated to investigate the possible changes in natural frequencies. The finite element model was also updated upon the changes in thickness and density of the truncated cones, and consequently, comparison between the theoretical and experimental natural frequencies was carried out again. Also, micrographs of two truncated cones were taken before and after autoclave treatment to examine the autoclave effect on the quality of the samples.
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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) | 0.002 | 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".