Ratcheting Response of Heat-Treated Notched 1045 Steel Samples Undergoing Asymmetric Uniaxial Loading Cycles
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Bibliographic record
Abstract
The present study evaluates the ratcheting response of notched cylindrical samples made of 1045 steel alloy subjected to asymmetric loading cycles using the kinematic hardening framework, coupled with Neuber’s rule. Test samples with V-shaped and semi-circular edge notches were first heat-treated under different conditions, resulting in various material hardness values at the notch root region. Local ratcheting at the notch root of samples was found to be highly dependent on the notch shape and the heat treatment conditions. HT1 samples with a lower hardness of 12 RC at the notch region possessed higher values of ratcheting, while ratcheting at the notched region for HT2 samples with 40 RC dropped to half of that in HT1 samples. The higher hardness of 50 RC at the notch edge of HT3 samples promoted the initial yield strength and the yield surface through the kinematic hardening rule with a larger translation into the deviatoric stress space as compared with samples HT1 and HT2 with 12 and 40 RC, respectively. The local ratcheting strain in sample HT1, with semi-circular notches (Kt=1.65) at a stress ratio (Smax/Sult) of 0.965, remained below 1.80% during the first hundred loading cycles. The local ratcheting decreased to 1.2% for sample HT2 and further dropped to 0.9% for sample HT3. The yield surfaces were translated consistent with the magnitude and direction of the backstress increments, as the applied loading excursion exceeded the elastic limit. Through the use of the Ahmadzadeh–Varvani (A–V) hardening rule, the predicted ratcheting values at notch roots were found to be larger in magnitudes as compared with those of experimental data, while the predicted local ratcheting through the Chaboche (CH) hardening rule fell below the experimental data. Results consistently showed that as sample hardness increased, the local ratcheting at notch roots decreased.
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Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 0.000 |
Machine scores (provisional)
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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 it