Dimple Grinding Coupled with Optical Microscopy for Porosity Analysis of Metallic Coatings
Bibliographic record
Abstract
Dimple grinding is one of the steps used in a common method of preparing samples for transmission electron microscopy (TEM); the TEM sample preparation process also involves ion beam sputtering after the dimpling stage. During dimpling, a spherical depression is machined into the sample, leaving a thicker rim to support and facilitate sample handling. In this paper, an alternative application for dimple grinding is developed; dimple grinding combined with optical microscopy is utilized to quantify internal porosity present within coatings. This technique essentially permits three dimensional porosity quantification across the coating thickness using a simple polishing method which provides analysis of areas larger than those observed during standard cross sectional microscopy. The application of this technique to nine electroless nickel-phosphorus (Ni-P) coatings deposited on Mg substrates is demonstrated. An analysis linking medium P content in the Ni-P coatings and high coating thickness to lower porosity is also performed. The lowest porosity was observed for medium P content coatings (5.2 wt% P), while the largest porosity occurred for the high P content coatings (10.0 wt% P). Porosity levels decreased continuously with increasing coating thickness (from 28 µm to 57 µm).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".