Coating growth behavior during the plasma electrolytic oxidation process
Bibliographic record
Abstract
In this study, aluminum oxide was deposited on an Al alloy substrate to produce hard ceramic coatings using a plasma electrolytic oxidation (PEO) process working at atmospheric pressure. The process utilizes dc and unipolar pulsed dc in the frequency range 0.2–20 kHz. Optical emission spectroscopy was employed to study the species and electron temperature of the plasma. The morphology and microstructure of the coatings were investigated using scanning electron microscopy. It was found that in the first 12 min of the PEO process, the plasma electron temperature increased with the applied voltage during the experiments, the plasma electron temperature was found to be in the range 4000–9000 K, and the applied voltage to the electrodes ranged up to 550–600 V for the different current modes. The plasma temperature profile exhibits a wider peak temperature spike for the dc power mode than for the pulsed dc mode, indicating that the dc plasma discharges would provide longer sintering time. The pulsed dc mode increases the spike temperature up to 8700 K but does not necessarily enhance the coating growth. The high spike temperature generated by strong discharges likely melts the oxide and then traps gas into the melt pool, resulting in some porosity at the interface. By eliminating the high temperature spike, a denser interface layer and homogenous coating morphology are produced.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".