Plasma nitriding design for aluminium and aluminium alloys
Bibliographic record
Abstract
Plasma nitriding for aluminium and aluminium alloys is a promising processing to improve the wear resistance for automotive parts. Normal plasma nitriding is characterised by three processes: presputtering, aluminium nitride nucleation and nitrided layer growth processes. N2+ presputtering is used to effectively eliminate the preexisting oxide films of Al2O3, covering the surface of aluminium matrix. Relatively long incubation time is required for nucleation process to form AlN islands or nodules on its surface. In addition, formation rate becomes very slow owing to low nitrogen diffusion coefficient in the nitrided layer. Physical and chemical modification methods to this normal nitriding processing are proposed to accelerate the formation rate of nitrided layer. Refinement of grain size in the aluminium matrix increases the formation rate by enlarging grain boundary area as a diffusion path. Crystallographic coherency between TiN and AlN reflects on enhancement of nucleation process by coformation of TiN with AlN. Standing on the nitriding design by physical and chemical modification of inner nitriding mechanism, an alternative plasma nitriding is proposed as the third processing for copper bearing aluminium alloys. In this processing, reduction of duration for nucleation and acceleration of growth rate are attained with the aid of the precipitate, Al2Cu. Crystallographic coherency between AlN and Al2Cu is effective to enhance the formation of AlN nodules and islands. Solid state reaction between Al2Cu and penetrating nitrogen is also significant to form the fine interfacial boundaries as a nitrogen diffusion path and to accelerate the formation rate of nitrided layer.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".