Microstructure-Based Computational Fatigue Life Prediction of Haynes 282 Alloy
Bibliographic record
Abstract
In this research, microstructure-based modeling is conducted to predict the fatigue crack nucleation life of a nickel-based alloy, Haynes 282, at different strain amplitudes from high cycle fatigue (HCF) to low cycle fatigue (LCF). A three-dimensional (3D) polycrystalline aggregate is constructed as the material representative volume element (RVE) using Voronoi tessellation with grain orientations assigned by random functions. The Hill’s yield criteria and linear strain hardening are employed to investigate the anisotropic plastic deformation in each grain using the finite element method (FEM), with the associated parameters determined by matching the monotonic stress–strain relationship and cyclic hysteresis loops of Haynes 282 alloy on the macroscopic scale. The fatigue crack nucleation life of Haynes 282 alloy is predicted using the Tanaka–Mura–Wu (TMW) model based on the material surface energy, shear modulus, Burgers vector and the plastic strain range at the microstructural level. It is demonstrated that this approach is able to predict the fatigue crack nucleation life of Haynes 282 alloy and estimate the scattering of the fatigue life by numerical simulations with different sets of grain orientation distribution functions. The results of the model prediction are in good agreement with the experimental observations. Furthermore, the effect of grain orientation on fatigue crack nucleation is discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".