Computational Prediction of Total Fatigue Life With an Integrated Approach
Bibliographic record
Abstract
Abstract This research tackles the fundamental issue of computational fatigue studies by developing an effective approach that combines the crystal plasticity finite element method (CPFEM) with the Tanaka–Mura–Wu (TMW) model for crack nucleation and the Tomkins model for fatigue crack propagation, to provide Class-A predictions of the total coupon-fatigue life (crack initiation and growth lives) for a nickel-based superalloy, Haynes 282. To gain a statistical significance accounting for the microstructure inhomogeneity, 11 3D Representative Volume Elements (RVEs) are created utilizing Dream.3d to represent the polycrystalline material with different grain structures and orientations in equivalence to the experimental microstructure data. The CPFEM model is calibrated to the material's hysteresis behavior, and then, the microstructural plastic strain from the RVE is taken to calculate the fatigue life. The prediction is found in good agreement with the fatigue test data, validating the effectiveness of the proposed approach in predicting the fatigue life and scatter due to microstructural variability for Haynes 282 alloy. In addition, the effects of local grain attributes including grain orientation and adjacent grain arrangement on fatigue crack nucleation are analyzed quantitatively. It is suggested that grain orientation influences plastic deformation by inducing the active slip systems, and the slip transfer across grain boundaries also contributes to fatigue crack nucleation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".