Effect of Carburizing and Nitriding on Fatigue Properties of 18Cr2Ni4WA Steel in Very High Cycle Fatigue Regime
Bibliographic record
Abstract
The work aims to study the influence of carburizing and nitriding on fatigue properties of 18Cr2Ni4WA high strength steel in very high cycle fatigue regime. Very high cycle fatigue tests were carried out on 18Cr2Ni4WA Steel after carburizing and nitriding respectively. The micro morphology of fatigue fracture was observed by scanning electron microscope, the failure mode and failure mechanism were discussed. The relationship between fatigue life and defect size, FGA size, fish eye size of fracture was analyzed. The characteristic size of defects is evaluated by Gumbel, Weibull and GEV distribution functions, and a modified Akiniwa fatigue life prediction model considering the relationship between FGA size and inclusion size was established. The results showed that, nitriding and carburizing treatment improve the surface fatigue limit of the steel. The fatigue life decreases with the increase of internal defect size and FGA size. After carburizing and nitriding treatment, the internal fatigue strength of the specimen decreases slightly. When the failure probability is 99%, the internal defect sizes of nitrided specimens calculated by Weibull, Gumbel and GEV distributions are 141.5 μm, 148.4 μm and 211.7 μm respectively. The calculated internal defect sizes of carburized specimens are 47 μm, 67.8 μm and 40 μm respectively. Compared with the experimental data, the fatigue strength predicted by GEV is the most appropriate. carburizing and nitriding treatment can improve the surface fatigue strength of 18Cr2Ni4WA steel, but slightly reduce the internal fatigue strength. The prediction result of the new model is conservative when the failure probability is 99%, which is suitable for engineering application.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".