Effect of Nitriding Time on the Structural Evolution and Properties of Austenitic Stainless Steel Nitrided Using High Power Pulsed DC Glow Discharge Ar/N2 Plasma
Bibliographic record
Abstract
A high power pulsed DC glow discharge plasma (HPPGDP) system was employed to perform fast nitriding of AISI 316 austenitic stainless steel in Ar and N2 atmosphere. In-situ optical emission spectroscopy and Infrared pyrometer measurements were used during the plasma nitriding to investigate the effect of dynamic plasma on the nitriding behaviour. SEM and EDX, XRD, Knoop indentation, and tribo-tests were used to characterise microstructures and properties of the nitrided austenitic stainless steel samples. HPPGDP produced high ionization of both Ar and N2 in the plasma that corresponded to dense ion bombardment on the biased steel samples to induce effective plasma surface heating and to form high nitrogen concentration on the biased steel surfaces, and therefore fast nitriding (> 10µm/hour) was achieved. Various phases were identified on the nitrided stainless steel samples formed from a predominantly a single phase of nitrogen supersaturated austenite to a multi-phase structure comprising chromium nitride, iron nitride and ferrite dependent on the nitriding time. All the nitrided AISI 316 austenitic stainless steel samples were evaluated with high hardness (up to 17.3 GPa) and exceptional sliding wear resistance against hardened steel balls and tungsten carbide balls.
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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".