Capillary Characterization of Fibrous Reinforcement and Optimization of Injection Strategy in Resin Transfer Molding
Bibliographic record
Abstract
During composite manufacturing, minimizing the residual void content is a key issue to ensure optimal mechanical performance of final products. For injection processes such as Resin Transfer Molding (RTM), the impregnation velocity has a direct impact on void creation at the flow front by mechanical entrapment of air bubbles. Previous work proposed to study capillary imbibition in fibrous reinforcement to determine optimal filling conditions during practical manufacturing. The objective of this study is to investigate further this possibility. For that purpose, an improved experimental procedure is proposed to estimate the optimal impregnation velocity from capillary rise tests and understand its effect in parts of varying geometry. Capillary rise experiments were carried out with an enhanced experimental protocol, and a new post processing technique was evaluated to analyze the results. The position of the capillary flow front was then used to deduce the optimal impregnation velocity range based on the Lucas-Washburn flow model. A series of injections were also carried out with a laboratory scale RTM mold to study the influence of flow velocity on the residual void content. Results show that the prediction from capillary characterization is close to the optimal velocity value deduced from manufacturing experiments. The study also highlights the importance of void transport during processing and suggests that the injection strategy (i.e., flow rate history) and the mold configuration (i.e., divergent versus convergent flow) are important process parameters that may influence void content and cycle time.
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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 | 0.000 | 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) | 0.000 | 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".