Diagnostics for advanced materials processing by plasma spraying
Bibliographic record
Abstract
Abstract Advanced coatings deposited by plasma spraying are used in a large variety of industrial applications. The sprayed coatings are employed typically in industry to protect parts from severe operating conditions or to produce surfaces with specific functions. Applications are found in many industrial sectors such as aerospace, automobile, energy generation, and biomedical implants. Coatings are built by the successive deposition of molten or partially molten particles that flatten and solidify upon contact on the substrate, forming lamellae. The coating properties are intimately linked to the properties of these lamellae, which in turn depend on in-flight particle properties as well as substrate temperature during spraying. Consequently, the development of diagnostic tools for monitoring and controlling these spray parameters will help provide the necessary information to study the coating formation process, optimize the coating properties, and, eventually, control the spray process in production. In this paper, a review of some recent developments of optical diagnostic techniques applied to monitor plasma-sprayed particles is presented. In the first part of the paper, two different sensing techniques for in-flight particle measurement are described. First, time-resolved diagnostics on individual particles is described. This technique is used to study the instabilities of the particle characteristics associated with the plasma fluctuations. Secondly, a technique adapted for use in an industrial production environment for measuring the particle jet characteristics as an ensemble is presented. In the second part of the paper, the use of an optical system to study the influence of the substrate temperature on the flattening and solidification of sprayed particles impacting on a flat substrate is described. The last part of this paper describes the optimization of nanostructured coatings based on a tight control of the temperature and velocity of the plasma-sprayed particles.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".