Elastic-Plastic Finite Element Analysis of Double-Edge Notched Fatigue Tests
Bibliographic record
Abstract
The ASME Boiler and Pressure Vessel Code (B&PV) Committee, Subgroup on Design Analysis, established the Task Force on Elastic-Plastic Finite Element Analysis (TF-EP-FEA) to investigate how to use EP-FEA to assess ASME B&PV Code stress limits. The present paper presents results of an investigation by the TF-EP-FEA to apply EP-FEA to predict cyclic life of double-edge notched fatigue specimens. EP-FEA results are compared to fatigue test results for two notch root radii in 304 stainless steel double-edge notched flat bars loaded in cyclic tension-compression. An analytical exercise was defined such that several TF-EP-FEA members started from the same problem definition and used the ASME Code and an EP-FEA program to compute the cyclic life of the double-edge notched fatigue specimens. The results and techniques used by the investigators are compared and conclusions reached regarding which method correlated best for this problem. Commonalties in the approaches are listed that may be used to develop general guidelines for use of elastic-plastic FEA to show satisfaction of the fatigue design rules of the ASME B&PV Code.
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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 | 0.001 |
| 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".