Shot peen forming pattern optimization to achieve complex target shapes: the inverse problem
Bibliographic record
Abstract
The inverse problem of determining how to shot peen a plate such that it deforms into a desired target shape is a challenge in the peen forming industry. While peening thick plates uniformly on one side results in a spherical shape, with the same curvature in all directions, complex peening patterns are required to form other shapes, such as cylinders and saddles found on fuselages and wing skin panels. In this study, we present an optimization procedure to automatically compute shot peening patterns. This procedure relies on an idealized model of the peen forming process, where the effect of the treatment is modeled by in-plane expansion of the peened areas, and on an off-the-shelf optimization algorithm. For validation purposes, we peen formed three 305 X 305 X 4.9 mm and two 762 X 762 X 4.9mm 2024-T3 aluminium alloy plates into cylindrical and saddle shapes using the same peening treatment. The obtained shapes qualitatively match simulations. For 305 X 305 X 4.9mm plates, the relative differences had the same distribution and were of the same order of magnitude as initial out-of-plane deviations measured on the as-received plates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".