Effect of Salt Bath Nitriding and Reoxidation Composite Texture on Frictional Properties of Valve Steel 4Cr10Si2Mo
Bibliographic record
Abstract
In order to improve the service life of 4Cr10Si2Mo valve steel, laser processing technology was used to prepare triangular textures with different area occupancies on the surface of 4Cr10Si2Mo, and then 4Cr10Si2Mo was subjected to salt bath nitridation (salt bath temperature 580 °C) and oxidation (oxidation temperature 400 °C). The mechanism of composite surface treatment technology on friction and wear performance of valve steel was explored. The effect of triangular texture on working surface stress and hydrodynamic pressure was explored using simulation technology, and the mechanism of texture in friction was further studied. The XRD test results showed that after salt bath nitriding and reoxidation treatment, the surface of 4Cr10Si2Mo mainly contained Fe2N oxide film and Fe3O4 and other components. The XPS test showed that the nitriding layer contained Cr-N, and the surface hardness reached 710.5 HV0.5. The simulation results showed that introducing surface texture will increase the stress on the contact surface, especially near the texture. However, under lubricating conditions, the flow field in the textured lumen produces a wedge effect, which increases the oil film pressure. After salt bath nitriding composite texture treatment, the wear resistance of 4Cr10Si2Mo significantly improved under the synergistic effect of the nitrided layer dominated by the magnetite phase (Fe3O4) and the microtexture. Fe3O4 can reduce the friction coefficient and resist oxidation reactions. In addition, the texture of the area occupancy of the texture also affects the surface tribological properties. The texture with an area occupancy rate of 11.45% (low × high is 0.3 mm × 0.3 mm) had the best anti-friction effect, and the friction coefficient reduced by 65%.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".