A Radial Basis Function Artificial Neural Network Methodology For Short And Long Fatigue Crack Propagation
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".