Critical Strain and Damage Evolution for Crack Growth From a Sharp Notch Tip of High-Strength Steel
Bibliographic record
Abstract
This paper proposes details of an approach that uses expressions of fracture strain and damage evolution as functions of stress triaxiality for notch-free specimens to predict their values for crack growth from a sharp notch tip of a single-edge-notched bend (SENB) specimen. Experimental testing and finite element (FE) modelling are used to determine the basic mechanical properties and deformation behaviour of those specimens, which are needed to calibrate model constants in the proposed approach and to validate prediction from the approach. Three types of mechanical testing were conducted, using standard smooth tensile, short-gauged tensile and standard SENB specimens. The FE modeling is to establish constitutive relationship between stress and strain for notch-free specimens so that the FE modeling can be used to determine parameters such as stress triaxiality and unloading modulus for the prediction of fracture strain and damage evolution at the sharp notch tip of SENB specimen. The study will then examine whether the proposed approach can predict the trend of variation for fracture toughness among three high-strength steels, which is an on-going study and the results will be presented in the conference.
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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) | 0.000 | 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 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".