New liquid processing of oxide/oxide 3D wowen ceramic matrix composites
Bibliographic record
Abstract
Abstract In this work, a novel process named Flexible Injection Process ( FIP ) was developed to manufacture near‐net shape oxide/oxide composites reinforced with 3D interlock fibers. This process uses a flexible membrane to apply pressure to promote transverse impregnation of the fibrous reinforcement by a slurry charged with sub‐micron ceramic particles. Due to the through‐thickness filtration and compaction, FIP process is much faster than typical in‐plane impregnation and results in composites with lower residual porosity than those produced by traditional processes. In this study, a mathematical modeling of the impregnation in FIP was developed and compared to experimental infiltration experiments. Furthermore, ceramic matrix composites ( CMC s) produced by FIP were compared to composites manufactured via an established RTM ‐like process. The two molding processes were compared to determine if the different flow behaviors have an impact on material densification, porosity formation, mechanical properties, and manufacturing time. CMC s produced by both methods resulted in similar microstructures, as determined by mercury intrusion porosimetry, even if FIP composites were marginally less porous. Finally, a comparison of mechanical properties resulting from the two manufacturing methods has shown a similar behavior. Thus, the main advantages of FIP molding were identified to be the shorter cycle time and the robustness of the impregnation compared to RTM ‐like processes.
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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 | 0.000 | 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".