Interactions between Superalloys and Mould Materials for Investment Casting of Turbine Blades
Bibliographic record
Abstract
The need of increased efficiency of industrial gas turbines comes also through the improvement of the composition of superalloys (addition of new solutes) and of the manufacturing technologies involved in the investment casting process of the turbine blades. Thus, the knowledge of the interactions between the ceramic materials used for casting and the molten superalloys must be deepened in order to minimize the formation of internal defects, to improve the casting surface and to optimize finishing and casting operations. In this work, a study of the wetting behaviour of some Ni- or Co -based superalloys, used for the fabrication of turbine blades, has been performed with reference to the interactions of these alloys in the molten state with the silica-aluminate based ceramic materials forming the shell or the core in the casting process. Wettability tests have been performed by means of the sessile drop method at 1500°C; the characterization of the interfaces between the molten drop and the substrates has been made on solidified sessile drop samples by SEM/EDS analysis to check the final characteristics of the interfaces. The results are discussed in terms of chemical interactions in relation to the processing parameters and as a function of the surface and interfacial energetic properties of the systems.
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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".