Edge Race-Tracking during Film-Sealed Compression Resin Transfer Molding
Bibliographic record
Abstract
Edge race-tracking is a frequently reported issue during resin transfer molding. It is caused by highly permeable channels and areas between the preform edge and cavity, which can significantly change the preform impregnation pattern. To date, information is scarce on the effect of edge race-tracking in compression resin transfer molding (CRTM). To close this gap, laboratory equipment was developed to study the CRTM preform impregnation via flow visualization experiments. The preform was thereby encapsulated in thin thermoplastic films sealing its impregnation. Film-sealed compression resin transfer molding (FS-CRTM) experiments of preforms with a small geometrical aspect ratio showed fast filling of the injection gap and a subsequent through-thickness preform impregnation. Creating an edge race-tracking channel, an additional lateral in-plane flow from the channel towards the preform center was observed, initiating soon after the injection started and caused by the spatial connection between the injection gap and the race-tracking channel. To diminish edge race-tracking, a passive flow control strategy was implemented via a split design of the upper tool to spatially isolate the injection gap from the channel and to pre-compact the preform edge. A delayed and reduced lateral race-tracking flow was observed, showing that the passive flow control strategy increases the process robustness of FS-CRTM regarding edge race-tracking effects.
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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 | 0.001 |
| 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".