Intimate contact development for automated fiber placement of thermoplastic composites
Bibliographic record
Abstract
The consolidation step in the automated fiber Placement (AFP) of thermoplastic composites is crucial for development of interlaminar bonding strength. Two mechanisms contribute to the interlaminar bond strength development in the interface between the incoming tow and the substrate: (a) the development of intimate contact between the two surfaces and (b) the formation of a fusion bond across those contacting surfaces known as healing. So far, a number of different theoretical intimate contact models have been proposed in the literature to study intimate contact development for different manufacturing processes. However, very limited experimental methods are available to measure degree of intimate contact. To fill this gap, a new experimental approach based on topology of the tape surface is introduced to quantify the degree of intimate contact for the AFP process. The new approach is based on the Bearing Area Curve (BAC) of the surface profile of the tape. To implement the new procedure, an unidirectional strips of carbon fiber/PEEK were laid down on the polished steel tool with different placement rates, compaction forces, hot gas torch (HGT) temperatures, and tool temperatures. The degree of intimate contact is calculated based on the proposed experimental approach and compared with prediction models from the literature. Furthermore, the effect of AFP process parameters on the degree of intimate contact is studied using BAC method and the effective intimate contact model. The results showed that the development of intimate contact is significantly governed by the torch and tool temperature.
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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 | 0.001 | 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".