Effects of Laser Shock Peening over Minimally Detectable Partial Through-Thickness Surface Cracks
Bibliographic record
Abstract
Abstract Laser shock peening (LSP) has evolved as a viable alternative to other surface treatments (shot peening, burnishing, etc.) that induce beneficial residual stress in structural components. While fatigue life improvements have been recognized by the aerospace industry, in-service components may have surface flaws with sizes below current inspection limits. A concern is that the application of LSP over an existing crack may cause unintended detrimental consequences. To address this concern, two different LSP processes were applied over a 0.25 in. (6.35 mm) partial through-thickness surface fatigue crack in 7075-T651 aluminum. The resulting residual stresses were measured using combinations of included X-ray diffraction with layer removal, incremental center-hole drilling, and neutron diffraction. Both LSP processes resulted in a through-thickness residual stress profile in which compressive stresses at either surface were balanced by tensile stresses in a band centered in the mid-thickness of the specimen. Peened and unpeened (baseline) specimens were then tested under constant-amplitude cyclic loading to assess the fatigue response. The average fatigue life of the baseline specimens was about 49,000 cycles. The peened specimens (both treatments) survived to runout, exhibiting no crack growth when examined with an optical microscope even when the crack tip was either very close to or inside the region of tensile residual stress.
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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) | 0.002 | 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".