A Model for the Inclusion of Notch Plasticity Effects in Fatigue Crack Growth Analysis
Bibliographic record
Abstract
Abstract Notches or other stress concentrations are by far the most common sites for the initiation and growth of fatigue cracks in aircraft structures. The growth of these cracks is directly influenced by the material stress-strain response in the vicinity of the notch. Specifically, when the applied (remote) stress is sufficient to cause local plastic deformation at the notch, the response (local) stresses can no longer be found using elastic stress concentration factors, and they become dependent on the prior loading history. This is to say that the response stresses can no longer be treated as state variables. The occurrence of fatigue crack growth at notches which experience local yielding one or more times during their design lifetime is, in fact, quite common in many cyclically loaded structures. Some of the assumptions inherent in “traditional” Linear Elastic Fracture Mechanics (LEFM) based fatigue crack growth analysis may be inappropriate for such problems. In particular, the assumption that the stress distribution on a critical plane remains proportional to the elastic distribution throughout the loading history becomes incorrect when one or more of the applied loads causes plastic deformation and introduces or alters a residual stress field in this region. This paper first describes an elastic-plastic stress-strain response algorithm which may be used to estimate response stress distributions on a critical plane on a cycle-by-cycle basis. This is followed by a discussion of the manner by which stress intensity factors may be calculated based on these response stress distributions using Green's functions. Finally, the use of these stress intensity factors for the calculation of crack growth rate and, ultimately, crack growth life, is demonstrated.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".