Recreating cobalt – based glaze layers through thermal spraying for extreme environments
Bibliographic record
Abstract
The demanding environments often encountered in engineering applications require the development of advanced materials capable of resisting to extreme conditions. Gas turbine engines is one example of application where tribological interfaces are exposed to extreme fluctuations in temperatures and harsh contact conditions. To overcome these challenges, materials and coatings are developed with specific characteristics tailored for the application. Certain materials attract special attention due to their capacity for developing specific tribolayers (i.e., glaze layers) during service at high temperatures, reducing their wear. For instance, cobalt-chromium alloys are strategically employed in gas turbine engines when temperature and wear are concerns due to their capacity for forming such lubricious glaze layers. Despite the protective effect of these glazes, their formation mechanism still relies on previous surface wear, making the break-in period of components challenging. More recently, the development of coatings based on the chemistry of these glazes has generated significant interest with the main purpose to be applied to protect other surfaces (e.g., nickel-based alloys) or to reduce the break-in period of cobalt-chromium alloys. Therefore, this study focuses on the development and analysis of a cobalt oxide thermally sprayed coating and its comparison to Haynes 25 and Inconel 718. Ball-on-flat at 600 °C and 800 °C tests were performed to evaluate the coatings' suitability for extreme environments. The results have shown a better performance of the cobalt oxide coating at lower temperatures and comparable performance to Haynes 25 at higher temperatures, where a glaze was formed over Haynes 25. More detailed analysis of the glaze layer formed on Haynes 25 revealed a mixed glaze formed with the debris originating from the Haynes 25 and the counterface (Inconel 718).
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".