Hybrid Ceramic Bearing Fatigue Testing for the Future Advanced Rotorcraft Drive System Program
Bibliographic record
Abstract
Today's rotorcraft transmissions predominately utilize fully metallic bearings where both the raceways and rolling elements are metal. Ceramic bearing materials offer the potential for meeting the demand for weight reduction and increased power-to-weight ratios. Hybrid ceramic bearings incorporate both ceramic and metallic components forming an assembly that lends itself to improvements in weight, corrosion resistance, reduced friction, and improved surface characteristics. Typically, hybrid ceramic bearings consist of ceramic rolling elements and metallic raceways. Hybrid ceramic bearings have demonstrated feasibility for both remotely and non-remotely monitored applications in rotorcraft drive systems. Characterization of potential material combinations of hybrid ceramic bearings is needed to guide the design of hybrid ceramic bearings for use in future and modified rotorcraft transmissions. This research, conducted under the Future Advanced Rotorcraft Drive System (FARDS) program, examined multiple bearing material combinations and characterized them in fatigue testing. Hybrid ceramic bearing material combinations showed increased performance when compared to fully metallic bearing material combinations. Favorable results indicate that hybrid ceramic bearings can have immediate impact on rotorcraft transmission designs.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".