Consolidation of curved composite parts manufactured by flexible injection
Bibliographic record
Abstract
Flexible injection with a compaction chamber is a new way of fabricating high performance composites by resin injection through fiber beds. This process belongs to the family of Liquid Composite Molding (LCM). It was originally devised to reduce cycle time for volume production. This technique consists of a closed rigid mold separated into two cavities by a flexible membrane. In a first stage, a given volume of resin is injected through a fibrous preform placed in the lower cavity. In a second stage, the upper cavity is filled with a pressurized fluid to complete the impregnation of the reinforcement. In a third and final stage, the saturated preform is consolidated. Flexible injection was shown to be very effective to reduce filling time as compared to classic Resin Transfer Molding (RTM). However, in the case of complex shapes, the flexible nature of the mold can lead to dimensional variations, especially in regions of strong curvature. This work presents implementation results of this new process for curved parts. A series of experiments have been carried out to demonstrate the viability of this new approach for L-shaped parts. Image analysis was performed on scanned images of cross-sections to assess the geometrical quality of the parts produced. Preliminary results indicate that the preforming stage has a direct influence on the geometry of the final part. Two types of defects can be observed in the parts: (1) a poor dimensioning of the preform creates resin-rich zones in the curved sections; (2) differences in compaction behavior between the flat and curved sections result in thickness variations. These observations suggest that a suitable preforming strategy should take into account the compaction behavior of the preform and the desired volume fraction of the final part.
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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 | 0.000 |
| 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) | 0.002 | 0.001 |
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".