Void content analysis and processing issues to minimize defects in liquid composite molding
Bibliographic record
Abstract
This investigation aims to study the impact of key process parameters dealing with the resin impregnation of fibrous reinforcements used in Liquid Composite Molding (LCM). The process parameters are the flow front velocity, the inlet mold pressure and the bleeding flow rate. The experimental setup consists of a computer‐assisted injection system and a Resin Transfer Molding (RTM) mold that allows monitoring the progression of the flow front and study the effects of resin bleeding and applying post‐fill resin pressure during cure (also known as “mold packing”). Three sets of RTM injections were carried out with a vinyl ester resin and a bidirectional 0°/90° E‐glass noncrimp fabrics under (1) constant injection pressure, (2) constant injection flow rate, and (3) bleeding as well as mold packing after filling. The quality of injected parts was evaluated by standard void content analysis based on ASTM burn‐off (D2734) tests. The experimental results are consistent with published data and with predictions of the optimal impregnation velocity obtained from capillary rise tests. This study also shows that the impregnation of fibrous reinforcements in LCM can be improved through various injection strategies, namely monitoring of the flow front velocity and specific post‐filling procedures. POLYM. COMPOS., 40:109–120, 2019. © 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 | 0.001 | 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".