Industrial Optimization and Efficiency for Processing of HVOF
Bibliographic record
Abstract
Optimising the efficiency of the thermal spray process is critical in industrial environments where high production rates that are linked to excellent quality control must be exploited. In this study, in-flight spray diagnostics have been employed to monitor the spray deposition of WC-10Co-4Cr and Cr₃C₂-25NiCr feedstocks (Oerlikon Metco AG, Switzerland) using a kerosene fuelled system (GTV HVOF K2, GTV Verschleißschutz GmbH, Germany). Two HVOF barrels, either of straight or conical internal bore, were employed with the goal of controlling the spray footprint geometry as well as maintaining consistent temperature and velocity conditions. The spray plume mapping (DPV2000, Tecnar Automation Ltd., Quebec, Canada) highlights that the conical expansion nozzle allows formation of a spray stream with more favourable deposition dynamics. Additionally the optimum thermal spray stand off distance could be objectively determined, as well as breakup of the composite feedstock monitored.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".