Thermostamping of [0/90]<sub><i>n</i></sub> carbon/peek laminates: Influence of support configuration and demolding temperature on part consolidation
Bibliographic record
Abstract
This work presents experimental results on the thermostamping of carbon/PEEK cross‐ply laminates made of unidirectional prepregs. The first objective was to evaluate the effect of the supporting frame and laminate thickness on the consolidation quality of the final part. Based on the use of a square frame mounted with constant‐force springs clamps, three support configurations are compared: (i) clamp a polyimide film with the springs and use it to support the blank, (ii) support the blank with the constant‐force springs without polyimide film and (iii) support both the blank and a polyimide film with the springs. Using the best of these configurations (configurations ii and iii), the second objective was to push further the analysis and evaluate how demolding at different temperatures affects the part thickness distribution, the interlaminar shear strength and the cycle time of the process. Good consolidation qualities were obtained with configurations (ii) and (iii). However, the latter allows for more uniformity in the part thickness and higher shear strength when demolding at higher temperatures. It also allows a reduction of the molding cycle time considering the part can be demolded at a higher temperature (reduced cooling time). POLYM. COMPOS., 39:3341–3352, 2018. © 2017 Society of Plastics Engineers
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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) | 0.002 | 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".