Development of Tailored Metallographic Preparation Techniques for Thermally Sprayed Coatings
Bibliographic record
Abstract
Abstract In order to properly characterize the entire deposition process, evaluation of the coating, including a reliable metallographic preparation technique which reveals the true microstructure, must be performed. Often, recommended metallographic sample preparation methods for thermally sprayed coatings are generic and are not tailored to specific materials. They are time-consuming and, in some cases, may provide inaccurate details (pull-outs, smearing, etc). This could lead to a wrong interpretation of the coating quality. The aim of the investigation was to develop new metallographic sample preparation procedures tailored to different types of coatings (metallic, ceramic, multilayer and composites), in order to reveal a more representative microstructure. A comparative study of different preparation procedures for the examination of various as-sprayed coatings is presented using an optical microscope. The coatings were deposited by atmospheric and vacuum plasma spray (APS and VPS) and high velocity oxygen fuel (HVOF) processes. A separate approach is recommended for choosing the right metallographic preparation procedure for ceramic, metallic, or composite coatings. Applied load and positioning of the mounted sample during preparation are identified as key factors in developing proper procedures. The microhardness of the coating must be considered when determining the applied load. Interesting practical trends in preparation procedures that may lead to superior coating representation and, in some instances, cost and time savings are presented.
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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".