Wear evaluation and microstructure analysis of cryogenically treated AISI 440C bearing steel
Bibliographic record
Abstract
In this study, two types of cryogenic treatment [deep cryogenic treatment (−196 °C) and shallow cryogenic treatment (−80 °C)] were used to increase wear resistance in AISI 440C bearing steel. Out focus was to find a way to increase wear resistance via deep microstructural analyses, and also to correlate the microstructure with the wear characteristics of specimens subjected to deep cryogenic treatment, conventional heat treatment, or shallow cryogenic treatment. Microstructural examinations of the specimens were performed using scanning electron microscopy, energy dispersive analysis of X-rays, and X-ray diffraction to study the wear characteristics of AISI 440C bearing steel. The results show that the specimens subjected to deep cryogenic treatment have greater wear resistance than the specimens subjected to shallow cryogenic or conventional heat treatment. The wear mechanisms included the formation and delamination of white layers. The microstructure of the steel was altered by the heat treatment process: the precipitation characteristics of the secondary carbides were modified and the levels of retained austenite were reduced, which correlated with the wear characteristics, and were identified as the potential mechanisms behind the increased wear resistance of the bearing steels due to the deep cryogenic treatment.
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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".