On the Influence of Ultrasonic Surface Mechanical Attrition Treatment (SMAT) on the Fatigue Behavior of the 304L Austenitic Stainless Steel
Bibliographic record
Abstract
The potential of ultrasonic surface mechanical attrition treatment (SMAT) at different temperatures (including cryogenic) for improving the fatigue performance of 304L austenitic stainless steel is evaluated along with the effect of the fatigue loading conditions. Processing parameters such as the vibration amplitude, the size, and the material of the shot medias were fixed. Treatments of 20 min at room temperature and cryogenic temperature were compared to the untreated material by performing rotating–bending fatigue tests at 10 Hz. The fatigue limit was increased by approximately 30% for both peening temperatures. Meanwhile, samples treated for 60 min at room temperature were compared to the initial state in uniaxial fatigue tests performed at R = −1 (fully reversed tension–compression) at 10 Hz, and the fatigue limit enhancement was approximately 20%. In addition, the temperature measurements done during the tests revealed a negligible self-heating (∆t < 50 °C) of the run-out specimens, whereas, at high stress amplitudes, temperature changes as high as 300 °C were measured. SMAT was able to increase the stress range for which no significant local self-heating was reported on the surface.
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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".