Investigation of Time-Dependent Instabilities of Plasma Spraying Process Using Online Diagnostic Systems
Bibliographic record
Abstract
Reproducibility of plasma spray process and resulting deposits is one of the most essential requirements for its applications 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 period plasma instabilities such as arc root fluctuation, process deviations may arise in longer time operations 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 or under periodic re-ignition 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".