Understanding The Effect of Shot Peening on Stress Corrosion Cracking of 316L Stainless Steel in High Temperature Water
Bibliographic record
Abstract
Shot peening is commonly employed as a surface treatment to introduce compressive residual stresses and reduce susceptibility to stress corrosion cracking. In this work, the effect of SP was compared to surfaces that were polished to oxide polishing suspension to evaluate the effect of strain distribution on stress evolution and cracking of 316L stainless steel. Environmentally assisted cracking tests were conducted in high temperature, lithiated (2 ppm of Li added as LiOH) hydrogenated water (3 ppm of H2) at 300 ℃, using tapered samples that were subjected to strain rate tensile test until 4.5% average plastic strain achieved over ~850 hours. <br/>Microstructural characterization of SP samples indicated a surface compressive stress up to 617 MPa and the material’s hardness increased significantly up to a depth of ~140 µm. However, microstructural characterization of SP did not reveal any phase transformation from austenite to martensite, but only the presence of an ultra-fine-grained layer (~2 µm thick) at the surface. <br/>Post-test characterization of the SSRT samples revealed that SP did not significantly enhance SCC resistance of 316L SS. On the contrary, comparing the results with OPS polished samples, the SP treatment exhibited a detrimental effect, lowering SCC resistance of 316L SS, on the zone with greater strain value during the slow strain rate tensile test. This was evident from a higher crack density on the SP surface compared to the polished surface. The cross-section analysis showed that the crack formed in the tapered sample were intergranular in nature. These results are discussed in the context of plastic deformation, work hardening, and stress redistribution from compressive to heavily tensile regions during deformation, impacting SCC susceptibility.
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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 | 0.001 | 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".