Effect of austempering temperature on microstructure evolution, mechanical properties and wear resistance of carbon-free nano-bainite steel
Bibliographic record
Abstract
Carbide-free nano-bainite steel holds significant potential for applications in mining as an ultra-high strength wear-resistant steel, but the wear mechanisms are not yet fully understood. This study aims to explore the effect of austempering temperature on microstructure, mechanical properties and wear resistance of carbide-free nano-bainitic steel. The relationship between the content and mechanical stability of retained austenite (RA) and mechanical properties was investigated to elucidate the mechanisms of pin-on-disc wear and impact wear. The results show that in the range of Ms∼300 °C, the phase transformation incubation period and completion time significantly increase with decreasing austempering temperature, which is mainly related to higher phase transformation driving force resulting from a larger subcooling degree and slow growth of carbon content in RA. Optimal strength and hardness of 2099 MPa and 615 HBW, respectively, are achieved after austempering at 210 °C for 83 h. Austempering at 270 °C for 11 h yields the best balance of strength and plasticity, with a strength-plasticity product of 28.8 GPa%. The micro-cutting mechanism of pin-on-disc wear leads to serious weight loss. The RA with poor mechanical stability accelerates the stress-induced martensite transformation, while internal stress support due to volume expansion reduces the penetration depth of abrasive particles. The weight loss in impact wear is primarily caused by shedding due to fatigue failure. With the increase of RA content and mechanical stability, the persistent stress-induced martensite transformation effectively hinders micro-crack propagation, thereby enhancing impact wear resistance.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".