Development and Validation of Novel FE Models for 3D Analysis of Peening of Strain-Rate Sensitive Materials
Bibliographic record
Abstract
In this paper, we provide two different symmetry cells to describe the shot-peening process. In this multiple impingement model, we study the dynamic behavior of TI-6Al-4V targets subjected to a large number of shots. Three-dimensional elastoplastic finite element analysis (FEA) of the process was conducted using these two symmetry cells for strain-rate sensitive targets and rigid shots. The basic symmetry cell is assigned a target surface area C×C, where C is one half of separation distance between adjacent shots. The second “enhanced” symmetry cell is assigned a target surface area 2C×2C thus allowing higher density of impact point locations. Average residual stresses inside the target predicted by FEA were compared with experimental measurements using the hole-drilling technique. In order to do this, a new averaged technique was developed to obtain the stress distribution inside the symmetry cell. The results reveal that both symmetry cell models could be used for shot-peening modeling. However, the use of the enhanced symmetry cell leads to a better agreement with the measured residual stresses. In addition, the enhanced symmetry cell model allowed us to overcome some of the shortcomings of the basic symmetry cell for cases involving high peening velocity and intensity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".