Residual Stress Field of a Single Edge Notch Specimen after Laser Shock Peening and Shot Peening Treatment
Bibliographic record
Abstract
Abstract Laser shock peening (LSP) is a reliable, repeatable, and successful surface technique for introducing high magnitude, deep compressive residual stresses that can significantly increase the fatigue life of metallic components. However, depending upon how the LSP treatment is applied, the induced residual stresses can result in the undesirable deformation of the part. In this work, traditional shot peening has been applied over LSP as a means to optimize the stress distribution at the surface of a part while constraining deformation. A single edge notch test specimen of AA7075 was laser peened local to the notch region and then shot peened over the entire central region. The resulting residual stress distribution has been characterized using neutron diffraction to measure the stress distribution in the bulk, and it was compared with (1) incremental center hole drilling to measure the stress distribution up to depths of ∼1 mm and (2) near-surface stresses obtained in a previous X-ray diffraction (XRD) study on nominally identical specimens subjected to the same surface treatments. For regions where the two techniques overlap, the residual stresses are in good agreement (within uncertainty). Comparing the bulk stresses obtained from neutron diffraction in this study and XRD data published elsewhere, it can be shown that shot peening applied after LSP has a profound effect on near-surface stresses; however, these effects disappear at depths of ∼0.7 mm or more.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".