A numerical method for elasto-plastic notch-root stress–strain analysis
Bibliographic record
Abstract
In this article, a computational modeling method of the multiaxial stress–strain notch analysis has been developed to compute elasto-plastic notch-tip stress–strain responses using linear elastic finite element results of notched components. Application and validation of the multiaxial stress–strain notch analysis model were presented by comparing computed results of the model to the experimental data of SAE 1070 steel notched shaft subjected to several nonproportional load paths. Based on the comparison between the experimental and computed strain histories, the elasto-plastic stress–strain model predicted notch strains with reasonable accuracy using linear elastic finite element stress histories. The elasto-plastic stress–strain notch analysis model provides an efficient and simple analysis method preferable to expensive experimental component tests and more complex and time-consuming incremental nonlinear finite element analysis. The elasto-plastic stress–strain model can thus be employed to perform fatigue life and fatigue damage estimates associated with the local material deformation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".