Load Normalization Method Accounting for Elastic and Elastic-Plastic Crack Growth
Bibliographic record
Abstract
Abstract A single specimen technique to estimate crack length, standardized in ASTM E1820, is the so called load-normalization technique, also known as the Key-curve technique. The method is based on the separability between deformation and crack length. This means that if the load is normalized by a suitable function of crack length, the result will be a single crack length independent load-displacement curve. If this “Key”-curve is known, then based only on load and displacement information it is possible to estimate the corresponding crack length. The load normalizing method assumes a plastic response of the specimen during crack growth. If there is crack growth already in the elastic regime, non-linearity in the load-displacement record is not due to plasticity, but due to the crack growth. In this case the standard load-normalization method does not work since it assumes that the non-linearity is due to plasticity or crack tip blunting. Such materials require a modified approach. Here, a modified load normalization method accounting for possible elastic crack growth is presented. The method is shown to produce realistic crack growth estimates regardless of plasticity level of the specimen. The method applies an improved load normalization equation compared to the one presently used in ASTM E1820.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".