Aspects of in-situ consolidation of thermoplastic laminates manufactured by Automated Tape Placement
Bibliographic record
Abstract
This paper focuses on the manufacturing of thermoplastic composite PEEK parts using Automated Tape Placement (ATP) with in-situ consolidation. The choice of the adequate processing parameters for the in-situ ATP is critical because no additional autoclave step is used to correct any manufacturing imperfections created during layup, or to homogenize the laminate consolidation. The current paper can be considered as a first step towards understanding the deformation behaviour of APC-2/IM7 (Cytec) in-situ ATP tape material subjected to various processing conditions. Compression analyses performed using a Dynamic Mechanical Analyzer (DMA) show that the material can quickly reach a steady state transverse deformation. The deformation behaviour of the pre-impregnated carbon/PEEK tape was investigated using a specially developed compaction apparatus. Processing parameters such as temperature, consolidation forces, and orientation of the deposited tow were independently controlled and the material's steady state deformation was measured by optical microscopy. The processing temperature was found to have negligible impact within the studied range. The consolidation pressure and ply orientation were found to contribute with similar magnitude to the tow deformation.
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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".