Methodologies And Applications Of Precision Shot Peening To Hole Or Slot Geometry
Bibliographic record
Abstract
Shot peening, a commonly used method of fatigue and surface enhancement, is a critical process in many of the components in the aerospace industry. When it comes to small holes and/or slot geometries in these components, the process can become a challenge when these features are smaller than a conventional size Almen strip. In these cases, a decision must be made about how to process this type of geometry. Oftentimes, the method to apply the shot peen process is already defined in a process specification or part print so we must adhere to that method. If a method is not defined however, there are a few different strategies we can use to apply the shot peen process to the hole/slot geometry as accurately as possible. These methods involve using full size Almen strips, partially shaded strips and mini sized Almen strips. Along with the various strip configurations, we will use a conventional nozzle peening process as well as deflector lance peening processes to the strips and then to coupons. By manufacturing identical coupons made from a commonly used aerospace alloy, we can apply each of these different shot peen strategies which are described in this study. After shot peening the coupons, they will be sent for x-ray diffraction (XRD) analysis. This will allow us to compare the compressive residual stress (CRS) profiles of each method to discern if there are any measurable differences between the methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".