Local Ratcheting and Stress Relaxation at Notch Root of Steel Samples undergoing Asymmetric Loading Cycles
Bibliographic record
Abstract
<p>Engineering components undergoing severe asymmetric loading cycles result in plastic strain accumulation referred as ratcheting. The excess of plastic strain induces damage leading to catastrophic failure in components. The present study intends to evaluate ratcheting response of 1045 steel and 316 stainless steel samples at the vicinity of notch roots with different notch sizes/shapes under various step loading conditions.</p> <p>To assess progressive ratcheting response and stress relaxation concurrently, the Ahmadzadeh-Varvani (A-V) and Chaboche (CH) kinematic hardening rules were coupled with Neuber’s rule through an algorithm to calculate local stress at the vicinity of notch root in steel samples. Finite element (FE) method was further employed to numerically assess local ratcheting around the notch root through use of ABAQUS software. The predicted results by the A-V and CH hardening rules and simulated outcomes of finite element were later compared with experimental data tested under various uniaxial loading levels, steps, and sequences. Local ratcheting strain values at the vicinity of notch root were found noticeably larger than nominal ratcheting values measured at farther distances from notch edge through use of strain gauges. The higher cyclic stress levels applied at notch root accelerated shakedown over smaller number of cycles and resulted in lower relaxation rate. Notch with larger diameters possessed smaller stress concentration factors and induced smaller plastic zone at notch root promoting ratcheting progress with less materials constraint over loading cycles as compared with notches with smaller diameters.</p> <p>Local ratcheting response was examined at the vicinity of different notch shapes and sizes as notched samples were tested at different Low-High-High (LHH) and High-Low-Low (HLL) loading sequences. The change of stress level from low to high promoted ratcheting over proceeding cycles while ratcheting strains dropped in magnitude for opposing sequence where stress level dropped from high to low. Predicted ratcheting curves by means of the A-V and CH frameworks as well as those simulated results through FE method resulted in the highest, average and lowest local ratcheting results for steel samples consist of respectively X-ellipse, circular and</p> <p>Y-ellipse notch shapes. The choice to employ a method of assessment to effectively evaluate local ratcheting response was found to highly dependent on the complexity of approach and related terms, variables and coefficients.</p>
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How this classification was reachedexpand
Full frame distilled prediction
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".