Three‐dimensional fatigue crack growth simulation and fatigue life assessment based on finite element analysis
Bibliographic record
Abstract
Abstract Damage tolerance design allows manufactures to assess fatigue crack propagation of components containing initial defects. A new modeling approach is proposed to carry out a systematic crack growth analysis based on an analytical method using reduced order models and three‐dimensional (3D) finite element (FE)‐based fatigue crack growth (FCG) analysis. 3D FE‐based FCG analysis is adopted as an advanced modeling approach for component geometries without any simplifications with respect to crack front shape or planarity of the crack path. The FE‐based FCG approach was verified and validated in two different stages: Ti‐6Al‐4V plate geometry with three different single elliptical crack configurations and Al 2024‐T3 specimens with two different multiple crack configurations. The analytical FCG approach was developed to estimate crack front evolution and crack growth life for three different elliptical cracks to verify the FE‐based approach. Experimental data of Al 2024‐T3 specimens with multi‐edge cracks was used to further validate the FE‐based FCG approach. The FE‐based FCG approach proved to be both accurate and powerful in assessing crack propagation life and crack paths of structural parts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".