Rolling contact fatigue behaviors of 25CrNi2MoV steel combined treated by discrete laser surface hardening and ultrasonic surface rolling
Bibliographic record
Abstract
In this paper, the effect of a novel surface treatment method that combing the discrete laser surface hardening (DLSH) and ultrasonic surface rolling (USR) on the material properties (surface roughness, microstructures, microhardness and residual stress) and rolling contact fatigue (RCF) behaviors of 25CrNi2MoV steel were investigated. A continuous-wave diode laser with a maximum output power of 2 kW was used to fabricate four different types of DLSH density samples with a size of Φ42 mm × 6 mm. The results showed that the combined USR treatment improved the surface quality (including roughness and oxide layer) of the DLSH samples, increased surface hardness, and obtained a beneficial surface compressive residual stress of up to 1240 ± 91 MPa. The severe plastic deformation introduced a gradient nano/ultrafine grain layer on both hardened zone (HZ) and substrate zone (SZ) surfaces of DLSH group samples, and the deformation depth decreased with the increase of DLSH density within 24.3–65.3 μm. Accumulation of plastic deformation below and around the HZ edge resulted in a gradient drop in hardness from HZ to SZ, which helps to relieve the occurrence of stress concentration at the surface HZ edge. Benefit from favorable factors, the RCF life of combined treated samples with hardened spot densities of 28%, 50%, 79% and 100% was increased by 82.2%, 123%, 143.6% and 171.9% compared with that of the untreated samples, respectively. After USR treatment, the failure mode within the SZ changed from spalling to delamination, but it remained as spalling failure within the HZ.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".