Effect of Pre-Oxidation and Thermal Exposure on the Microstructure of Superalloys and Thermal Barrier Coating Systems
Bibliographic record
Abstract
Thermal barrier coatings (TBCs) enable the nickel-base superalloy gas turbine blades of modern gas turbine engines to run at their highest possible temperatures.Currently, a bond coat (BC) is utilized to adhere the yttria-stabilized zirconia (YSZ) top coat to the nickel-base superalloy substrate.However, the addition of this manufacturing step increases cost.In this study, three nickel-based superalloys: IN-738, CMSX-4 and Rene N5 are considered.Isothermal oxidation studies were conducted at 1150°C in atmosphere for holding times ranging from 30 minutes to 16 hours.YSZ top coats were applied directly to the nickel-base superalloy René N5 isothermally oxidized for 2hrs at 1150°C in atmosphere.Rene N5 oxidized at this time and temperature produced the most adherent, dense and uniform α-Al 2 O 3 scale which was anticipated to enhance YSZ adhesion.YSZ coatings were then applied using suspension plasma spraying technology (SPS).Coatings of dense vertically cracked (DVC) and columnar morphologies were applied.Following YSZ application, the samples were subjected to a diffusion heat treatment in vacuum at 1080°C to promote YSZ/substrate adhesion.These samples were then cyclically and isothermally tested in atmosphere at 1150°C.Both SPS DVC and SPS Columnar coatings were found to have poor adhesion (delaminated in less than 25 cycles).The lack of adequate α-Al 2 O 3 coverage, the rapid formation of spinel and grit blasting prior to coating deposition were determined to be the primary causes of poor performance.
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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) | 0.002 | 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".