HVOF Process Optimization for the Erosion Resistance of WC-12Co and WC-10Co-4Cr Coatings
Bibliographic record
Abstract
Abstract Recent studies have demonstrated that WC-12Co and WC- 10Co-4Cr coatings were the best performing HVOF coatings against erosion. This paper looks at the influences of the HVOF process parameters for WC-12Co and WC-10Co-4Cr materials on the erosion resistance of the coatings. The effect of powder morphology, matrix chemistry and HVOF process parameters with respect to both silica slurry erosion and alumina dry erosion has been studied. All coatings were produced using the HVOF JP-5000 system with kerosene-oxygen flame. The spraying parameters were analyzed in term of sprayed particle velocity and temperature as measured with the DFV2000 optical diagnostic system. Simultaneously with in-flight particle measurements, the substrate-coating temperature was monitored by infrared pyrometry during coating deposition. The resulting coating microstructure was evaluated in terms of microhardness, porosity type and extent of wear damage after dry and slurry erosion. The material volume loss under various erosion conditions was related to the coating properties and microstructure. According to the experimental results, the following conclusions are drawn: 1) the kerosene flow rate affects the inflight particle state (velocity and temperature) and the coating porosity. 2) Cobalt-chrome matrix cermet performs better in slurry erosion while denser and harder cobalt matrix cermet performs better in dry erosion. 3) The use of kerosene-rich flame with lower oxygen stoichiometry reduces the carbide degradation and optimizes the wear performance of WC-12Co coatings in both dry and slurry erosion.
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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 | 0.001 |
| 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 | 0.001 | 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".