Effects of Composition on Melt Fillability and Impact Resistance of TiAl Alloys for Thin-Blade Turbine Wheels: Laboratory Predictions and Product Verification
Bibliographic record
Abstract
Scaling up the production of TiAl turbine wheels for passenger car turbochargers requires the fabrication of thin blades that are similar to those of nickel-based superalloys. To achieve this, the molten metal fillability and impact resistance of thin blades must be improved. In this study, the effects of composition on these properties are predicted using simple laboratory experiments with binary, ternary, and practical alloys and are then verified with actual turbine wheels. The melt fillability of the turbine wheel blade is predicted using the amount of molten metal passing through an Al2O3-1%SiO2 mesh. The binary alloy exhibits the best fillability, which is reduced by the addition of Cr and Si. Charpy impact tests on as-cast materials at 25 and 850 °C show that the addition of Cr and Mn improves the impact resistance, but the addition of Nb, W, Mo and Si reduces it. Therefore, the molten metal fillability and/or impact resistance of practical TiAl alloys containing such additives owing to other requirements are low and require improvement for use in thin-blade turbine wheel applications.
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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 | 0.000 | 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".