Investigation of Time-Dependent Instabilities of Plasma Spraying Process Using Online Diagnostic Systems
Bibliographic record
Abstract
Abstract Reproducibility of plasma spraying process and resulting deposits is one of the most essential requirements for its application in industrial environment. In most cases, only controlling process input parameters does not suffice for achieving stability and reproducibility of the process. Besides short time plasma instabilities such as arc root fluctuation, process deviations in longer time operations may arise due to electrodes wear and need to be identified by online diagnostics of the plasma spraying process. In this study two online diagnostic systems were employed to investigate the long-term behaviour of a F4-type plasma gun with a convergent and divergent Laval-nozzle. A Plasma Instability Analysis (PISA) system was applied to identify electrode wear and plasma fluctuation phenomena within the gun whereas Particle Flux Imaging (PFI) was used to correlate the electrode wear with the plasma jet characteristics at the nozzle exit. The long-term performance of electrodes was investigated under continuous operation and under periodic reignition of the torch. A good correlation between aberrations in the plasma process observed with PFI and changes in the frequency spectra detected by PISA was found.
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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 | 0.002 |
| 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 | 0.000 | 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".