Insights into the deposition of WC-10Co-4Cr and MCrAlX protective coatings using an internal diameter HVOF system
Bibliographic record
Abstract
Inner diameter high velocity oxygen fuel (ID-HVOF) thermal spraying is gaining increased attention as it offers new solutions for coating space restricted industrial parts where access with conventional HVOF torches is not feasible. Yet, challenges associated with using these newly developed torches have to be addressed for successful transitioning of ID-HVOF from an emerging thermal spray technology to an industrially viable process. Some of the challenges related to the compact size and low combustion powers of the ID-torches include spraying small size feedstocks at short standoff distances and controlling thermal load on the part. In this study we used a kerosene fuel ID-HVOF system for development of cermet and metallic coatings. WC-10Co-4Cr and MCrAlX (M=Ni, Co and X=Y, HfSi) compositions were selected in the context of development of protective coatings for wear and corrosion protection of landing gear and downhole component inner cylinders and high temperature protection of small and complex parts of gas turbine engines, respectively as these applications could greatly benefit from the ID-spraying technology. The development of parameters space for coatings’ deposition as well as the effect of spray parameters and correlated particle temperature, velocity and surface temperature on the coatings’ microstructure and properties will be discussed.
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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".