Optimization and Process Control for High Performance Thermal Spray Coatings
Bibliographic record
Abstract
Abstract Thermal spray coatings are used to protect surfaces against exposure to severe conditions. To insure a reliable protection, not only the structure and properties of the sprayed coatings must be optimized but also one needs to develop appropriate process control techniques to produce high performance coatings in a consistent manner, day after day. This is particularly important during plasma spraying as the wear of the electrodes affects significantly the plasma characteristics and consequently the coating properties. First, in this paper, the stability of plasma spray processes is investigated by monitoring in-flight particle characteristics and plasma fluctuations. Secondly, the possibility and advantages of controlling plasma spray processes by monitoring and regulating the condition of the sprayed particles are discussed. Finally, we will see how the properties of thermal barrier coatings and wear resistant coatings can be optimized by controlling the temperature and velocity of the sprayed particles both in the plasma spray and HVOF (high velocity oxy-fuel) processes.
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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) | 0.000 | 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) | 0.000 | 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".