Ratcheting Response of Heat-Treated Notched 1045 Steel Samples Undergoing Asymmetric Uniaxial Loading Cycles
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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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 | 0.000 | 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) | 0.002 | 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".