Metallic Fume Emission and Operator Exposure During WC-10%Co-4%Cr Spraying by HVOF
Bibliographic record
Abstract
Abstract The increasing use of HVOF (high-velocity oxy-fuel) coatings to replace hard chrome plating was initially motivated by the environmental and health risks associated with hexavalent chromium (Cr6+) emissions during the plating process. Following performance optimization and proper coating selection, it has been found that the potential increase in performance and the cost/process-time reduction offered by the HVOF process often justifies its application. Recently, the use of Cr-containing alloys processed by HVOF has drawn attention to the potential release of Cr6+ during heating of metallic chromium. For instance a new California regulation for airborne toxic control measures to reduce emission of hexavalent chromium from thermal spraying is in preparation. The present study focused on monitoring operator exposure during the HVOF spraying of WC-10%Co-4%Cr. The spraying was performed using a JP-5000 HVOF gun in a spray room in which a ventilation flow rate of 10,000 scfm was imposed. Air sampling was taken in the spray room as well as in the adjacent control room in accordance with the NIOSH 7300 and 7600 standard methods. A portable sampler attached on the operator’s chest was also used to monitor the operator exposure during a typical workday. Results indicate that even though metallic fumes of Co and Cr are present in the spray room during spraying, the hexavalent form Cr6+ is not detected. It was concluded that an operator entering the spray room for a limited amount of time with the gun in operation would be exposed to only low fume levels that can still be reduced by wearing an appropriate respiratory mask.
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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 | 0.000 | 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".