Three-dimensional elastoplastic finite element model for residual stresses in the shot peening process
Bibliographic record
Abstract
The fatigue life of mechanical components subjected to variable loading conditions is a crucial parameter in the design of automotive and aerospace mechanical components. The fatigue strength of the component can be improved by inducing compressive residual stress in the surface and subsurface layers by the shot peening process. This increases the failure strain magnitude for crack nucleation and propagation. Extensive experimental results have been published on the effect of shot peening process parameters on residual stress distributions and subsequently on fatigue performance. Numerical simulation of residual stres induced by the shot peening process is important for understanding and optimization of the process in terms of the residual stress profile and maximum shot coverage in the shortest time. In this paper, an elastoplastic finite element (FE) model will be presented to simulate the shot peening process in three-dimensional configuration. The FE model takes into account numerical and material damping, thermal elastic—plastic material behaviour, a rigid or deformable body for the shot and the effects of multiple shot impacts. The workpiece material modelled was AISI 4340 steel at HRC 52 ± 2. The effects of strain hardening, strain rate and temperature were considered in the non-linear material model. Results show that the predicted residual stress profile agreed well with that acquired from published experimental results provided the shot was modelled as a deformable body and material damping was considered.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".