Assessment of the Poisson Ratio Effect on Low Cycle Fatigue (LCF) Behavior of Shear-Cracking Mode Materials
Bibliographic record
Abstract
Abstract This paper evaluates the effect of different Poisson's ratios and an equivalent Poisson's ratio formula on the crack initiation angle and life estimation by using the three shear-strain based fatigue damage models (Brown–Miller, Fatemi–Socie, and Liu's Virtual Energy) on strain-controlled biaxial cyclic loading tests of specific tubular material specimens. The four shear failure mode materials studied were Inconel 718, 304L stainless steel, normalized 1050, and quenched/tempered 1050 steels. This study was motivated by the fact that for all the strain-controlled fatigue testing, the induced transverse strain was not measured, but assumed to be a negative product of the axial strain and the Poisson ratio, where the axial strain was known and given, and the Poisson ratio was either assumed to be a constant or a variable represented by a function of elastic and plastic equivalent strains. Therefore, the assumption of a Poisson's ratio in the transverse strain calculation was assessed in this study. It is concluded that the use of different Poisson's ratios in the three fatigue damage models would have some effects on the crack angle prediction, depending on the material type, and little effect on the fatigue life estimation for all materials investigated in this study.
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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 | 0.002 | 0.005 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".