Optimization of shot peening for titanium alloys Ti 10-2-3 in CONDOR project
Bibliographic record
Abstract
CONDOR is an R&D project lead by IRT-M2P with different industrial partnership to increase knowledge and simulation models of shot peening. This surface hardening process aims to perform different shots with high velocity on metallic surfaces to introduce compressive stresses on it. Fatigue behavior of shot peened parts is significantly improved. During this research project a DOE has been carried out to optimize shot peening parameters on titanium alloys (surface roughness before shot peening, size and shot’s hardness, covering and intensity). The DoE is composed by more than 300 fatigue specimens. All this data allows us to define specifically each shot peening parameter influence on shot penned parts efficiency. CONDOR project allows simulation development of models to simulate shot peening effect by taking into account the parameters introduced above. Those models are used to evaluate residual stress level and fatigue lifetime after shot peening and to confirm models readiness level. This study has defined optimized machining and shot peening conditions in order to increase parts fatigue lifetime.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".