Plasma nitriding design for aluminium and aluminium alloys
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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. N 2 + presputtering is used to effectively eliminate the preexisting oxide films of Al 2 O 3 , 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, Al 2 Cu. Crystallographic coherency between AlN and Al 2 Cu is effective to enhance the formation of AlN nodules and islands. Solid state reaction between Al 2 Cu 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it