Bibliographic record
Abstract
For high end composite manufacturing in a rapid development environment, the long lead item is often the hard tooling, in particular the cure mold. A traditional metal mold takes in the neighborhood of four to nine months to design, fabricate and validate. With high temperature capable print materials, and larger and faster printers, Additive Manufacturing (AM) appears to have high potential in this area of advanced composites manufacturing. Sikorsky has used AM very successfully on a scale up to approximately 3'x3' and cure temperatures of 350°F. Though long-term durability is still to be determined; the materials, technologies, and techniques Sikorsky has employed for AM autoclave cure molds on this scale have consistently exceeded expectations. AM tools along the scale of main rotor blades could be leveraged to realize even more significant cost and schedule gains from AM autoclave tooling, and in this area, there are still more questions than answers when it comes to a dependable tooling solution. Rotorcraft development, in particular Future Vertical Lift (FVL), programs offer an opportunity to realize the significant schedule and cost benefits AM can provide for composite tooling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".