Plasma Process to Harden the Surface of Aluminum and Alloys
Bibliographic record
Abstract
Aluminum and its alloys are valued in structural applications for their excellent strength to weight ratio and several other desirable properties. However, their tribological properties in sliding friction are poor due to the softness of the bulk combined with the fragility of the native oxide. A method to harden the surface of small aluminum components has been developed. The process consists in immersing the object to be treated in a pulsed low pressure plasma using oxygen as working gas, and applying to it a high negative voltage (typically 30 kV). This drives the oxygen ions of the plasma into the piece to a depth of tens of nanometers (∼10-6 inch). This results in the formation of a layer of extremely fine-grained oxide precipitates in an aluminum matrix. The thickness of the layer is of the order of 0.1 μm (4 × 10-6 inch) and the precipitate size much less. The optimum results are obtained with a layer composition of about 50% oxide and 50% metal. The hardness of the treated layer, as measured by nanoindentation, is increased several fold up to values of 3 - 5 GPa (400 - 700 kpsi), while retaining good elasticity and ductilily. Nanoscratch test results show reductions in the scratch depths and the friction coefficients by nearly the same factors.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".