Al-based functionally graded super-intermetallic compounds for the turbine blade of a high-performance jet engine
Bibliographic record
Abstract
Abstract An Al-based functionally graded structure is fabricated, featuring a discrete compositional gradient of 48.1Al47.9Ti4.0V/73.7Al24.2Ti2.1V/89.5Al10.0Ti0.5V in atomic percentage. This structure is produced via dual-hybrid laser powder bed fusion and directed energy deposition combined with computer numerical control milling. Particularly remarkable is the high tensile strength, ranging from 0.5 to 1.7 GPa. This strength is attributable to three key factors: (1) rapid solidification during inert gas flow following high-energy laser irradiation, (2) the formation of γ-like intermetallic matrix phases along with γ′-like (α 2 -based in the composition of 48.1Al47.9Ti4.0V) intermetallic precipitate phases, and (3) the presence of segregates and precipitates with more V-based compounds at the grain boundaries, distinguishable by their sizes, shapes, and distributions across the microstructures. In addition, (4) large anisotropically lamellar precipitate phases, several hundreds of nanometers in diameter, are predominantly observed in the dendritic regions. Owing to these Al-based intermetallic compounds, each exhibiting low densities (2.9−3.7 g cm −3 ) and high thermal resistances (450−900 °C), the functionally graded structure is then employed in the topological optimization of a turbine blade system for a high-performance jet engine. This process involves identifying the stress-bearing regions, removing any stress-free areas, and applying a structural-stiffness-increasing mechanism through shape and geometric transformation.
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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) | 0.001 | 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".