Modeling of Crack-Opening Stress Levels under Different Service Loading Spectra and Stress Levels for a 1045 Annealed Steel
Bibliographic record
Abstract
The crack opening stresses of a crack emanating from an edge notch in a 1045 annealed steel specimen were measured under three different Society of Automotive Engineers (SAE) standard service load histories having different average mean stress levels. The three spectra are the SAE Grapple Skidder history (GSH) which has a positive average mean stress, the Log Skidder history (LSH) which has a zero average mean stress, and the Inverse of the GSH (IGSH) which has a negative average mean stress. In order to capture the actual behavior of the crack opening stress in the material, the crack opening stress levels were measured using a 900X optical video microscope at frequent intervals for each set of histories scaled to two different maximum stress ranges. The crack opening stresses were modeled assuming that the crack opening stress when it is not at the constant amplitude steady state level for a given stress cycle builds up as an exponential function of the difference between the current crack opening stress and the steady state crack opening stress of the given cycle unless this cycle is below the intrinsic stress range for crack growth or the maximum stress in the cycle is below zero in which case the crack opening stress does not change. The crack opening stress model was implemented in a fatigue notch model and the fatigue lives of notched annealed 1045 steel specimens under the three different spectra scaled to several maximum stress levels were estimated. The average measured crack opening stresses were within between 8 and 13 percent of the average calculated crack opening stresses. The fatigue life predictions based on the modeled crack opening stresses and the fatigue notch model were in good agreement with the experimentally determined fatigue data.
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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 | 0.000 | 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) | 0.001 | 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".