Enhancing fatigue performance of austenitic stainless steel via warm surface severe plastic deformation using surface mechanical attrition treatment
Bibliographic record
Abstract
The effects of warm Surface Severe Plastic Deformation (SSPD) performed via Surface Mechanical Attrition Treatment (SMAT) on the microstructure, hardness, and residual stress gradients, as well as the resulting fatigue properties of a 316L austenitic stainless steel were investigated. Machined samples were ultrasonically shot peened for 10 min at Room Temperature (RT), 523 K, and 773 K before undergoing rotating-bending fatigue tests to determine the endurance limit. The RT-SMATed sample, for which machining grooves are removed by the shot impacts, showed a superior fatigue limit endurance than machined samples (+25 %), with subsurface nucleation sites. The 523 K peened samples revealed a similar fatigue limit endurance accompanied by the same type of subsurface crack nucleation. Due to the increased roughness and expansion of surface stress raisers by pile-ups and surface oxidation, the nucleation of the fatigue cracks occurred at the extreme surface when SMAT was done at 773 K. Despite the surface nucleation, SMAT carried out at 773 K provided a superior endurance limit (+15 % compared to RT-SMAT). This improvement was attributed to the restored microstructure formed under 773 K peening, which stabilizes the introduced compressive residual stress, and to the deeper and lower tensile peak induced by warm SMAT. To support the interpretation of fatigue behaviour under varying mean stress conditions, a Goodman analysis was conducted, confirming the beneficial role of compressive residual stress introduced by warm peening on endurance limit improvement.
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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".