Cold Spray MCrAlY Coatings on Single-Crystal Ni-Base Superalloys: A Substrate Perspective
Bibliographic record
Abstract
Abstract The hot-section components of modern gas turbines (e.g., turbine blades and vanes) are typically manufactured from Ni-base superalloys. To develop the γ/γ' microstructure that imparts superior thermomechanical and creep properties, Ni-base superalloys usually require three distinct heat treatments: first a solution heat treatment, followed by primary aging, and finally secondary aging. To achieve oxidation resistance, MCrAlY coatings are applied on the superalloy components as either environmental coatings or bond coats for thermal barrier coatings. In this study, the effects of different processing sequences on MCrAlY coating characteristics and short-term isothermal oxidation performance were investigated. Specifically, cold spray deposition of NiCoCrAlTaY coatings was carried out on single-crystal Ni-base superalloy substrates that underwent various degrees of the full heat treatments prior to being coated. The remaining required heat treatments for the superalloy substrates were then performed on the coated samples after the cold spray deposition. The microstructures of the CMSX-4 substrates and NiCoCrAlTaY coatings were characterized after each heat treatment. Isothermal oxidation performance of the coated samples prepared using different sequences was evaluated at 1100°C for 2 hours. The results suggested a promising procedure of performing only solution heat treatment on the superalloy substrate before coating deposition and then primary aging and secondary aging on the coated samples. This processing sequence could potentially improve the oxidation performance of MCrAlY coatings, as the aging processes can be used to effectively homogenize coating microstructure and promote a thin thermally grown oxide (TGO) scale prior to actual isothermal oxidation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".