Diagnostics for advanced materials processing by plasma spraying
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it