Through Process Modelling applied to the fatigue resistance of cast Aluminum
Bibliographic record
Abstract
The aim of this study is to evaluate a full integrated modelling strategy to evaluate the influence of casting defects on the fatigue life directly from process simulation. We have shown that defects characterized by their size and the microstructure characterized by the SDAS, are the main parameters that control the fatigue limit. A fatigue criterion that already takes into account for the effect of the defect on the fatigue limit was modified to introduce the effect of SDAS. This improved criterion has been employed to predict the Kitagawa diagram for multiaxial loading for different loading cases. The simulation of the modified criterion showed that the reduction of the fatigue limit with the defect size and SDAS is well described. In the last part a numerical model was developed to perform a simulation of the fatigue limit starting from the simulation of the casting process. Using this numerical model, we simulated the defect size and SDAS depending on the solidification time, the fatigue limit is simulated using the improved criterion. We proposed in this part a mold which let to obtain samples with two different microstructures. In this study, a second fatigue tests was carried out on these samples to validate the numerical simulation on the proposed mold. It turns out that the numerical model provides reasonably well the obtained experimental results.
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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 | 0.000 | 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".