A Radial Basis Function Artificial Neural Network Methodology For Short And Long Fatigue Crack Propagation
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Bibliographic record
Abstract
Fatigue damage process inherently has multiscale characteristics.As a result, fatigue cracks mainly classified as short cracks (SCs) and long cracks (LCs).It is necessary to quantify the fatigue crack growth (FCG) rate in both the short and long crack regimes.Especially in the case of lightweight alloys and high cycle fatigue in which short cracks' behavior dominates total fatigue life.There is still no proper model to characterize FCG rate in the SC regime.In the presented study, a radial basis function artificial neural network (RBF-ANN) model as a machine learning approach has been developed to quantify the FCG rate in both the SC and LC regimes.Experimental data sets of 2024-T3 and 7075-T6 aluminum alloys are employed to train and verify the model.The presented study showed that the RBF-ANN model can accurately predict the nonlinearity of FCG rate in terms of stress intensity factor range in both the SC and LC regime.However, the predictions showed that the extrapolation ability of the model is not as appropriate as its interpolation capability.In addition, density and distribution of the input data strongly affect the accuracy of the RBF-ANN model.
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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 | 0.001 | 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 it