Surface Texturing-Plasma Nitriding Duplex Treatment for Improving Tribological Performance of AISI 316 Stainless Steel
Why this work is in the frame
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Bibliographic record
Abstract
Surface texturing-plasma nitriding duplex treatment was conducted on AISI 316 stainless steel to improve its tribological performance. Tribological behaviors of ground 316 substrates, plasma-nitrided 316 (PN-316), surface-textured 316 (ST-316), and duplex-treated 316 (DT-316) in air and under grease lubrication were investigated using a pin-on-disc rotary tribometer against counterparts of high carbon chromium bearing steel GCr15 and silicon nitride Si₃N₄ balls. The variations in friction coefficient, mass loss, and worn trace morphology of the tested samples were systemically investigated and analyzed. The results showed that a textured surface was formed on 316 after electrochemical processing in a 15 wt % NaCl solution. Grooves and dimples were found on the textured surface. As plasma nitriding was conducted on a 316 substrate and ST-316, continuous and uniform nitriding layers were successfully fabricated on the surfaces of the 316 substrate and ST-316. Both of the obtained nitriding layers presented thickness values of more than 30 μm. The nitriding layers were composed of iron nitrides and chromium nitride. The 316 substrate and ST-316 received improved surface hardness after plasma nitriding. When the tribological tests were carried out under dry sliding and grease lubrication conditions, the tested samples showed different tribological behaviors. As expected, the DT-316 samples revealed the most promising tribological properties, reflected by the lowest mass loss and worn morphologies. The DT-316 received the slightest damage, and its excellent tribological performance was attributed to the following aspects: firstly, the nitriding layer had high surface hardness; secondly, the surface texture was able to capture wear debris, store up grease, and then provide continuous lubrication.
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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