Oxidation resistance of novel multilayer coatings for gas turbine components
Bibliographic record
Abstract
This article presents a study of the oxidation behaviour of two multilayer coatings, denoted as MC I and MC II, respectively, which are designed with a three-layer architecture: a Cr–Si barrier layer on the IN738 substrate, a NiCrAlY intermediate layer, and an aluminised top layer. The emphasis of this research is on the total amount of oxide scales formed and the residual oxide scales on the coatings with various combinations among the three layers to investigate the effects of each layer and the interactions between the layers on the oxidation resistance of the coatings. The difference between the two multilayer coatings is that the Al content in the top layer of MC II is twice as much as that in the top layer of MC I. The coatings are fabricated through a combination of plasma spray and pack cementation processes, and then experience oxidation test at 1050°C for 1000 h. The average thicknesses of the oxide scales are around 15 and 45 μm for MC I and MC II, respectively. The experimental results are modelled using the analysis of variance. Two models for the volume of total oxide scales and the ratio of the volume of spalled oxide scales to the volume of total oxide scales, are proposed to study the effects of the coating layers on the formation and spallation of oxide scales, which directly affect the oxidation resistance of the coatings. The experimental and analytical results demonstrate that the Cr–Si layer and its interactions with the other coating layers affect the spallation of oxide scales the most among the three layers. It prevents Al and Cr from diffusing into the substrate. The interaction between the Cr–Si layer and the NiCrAlY layer, and the increase in Al/Ni ratio of the aluminide top layer, promote the formation of oxide scales and also minimise the spallation of oxide scales.
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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) | 0.000 | 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 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".