Comparison Between Standard and Modified Back-Slotted DWTT Specimens
Bibliographic record
Abstract
The effect of fracture speed on the ductile fracture resistance of line-pipe steels can have an important effect in the basic understanding of the toughness requirements for crack arrest. Recently, the authors have extended the drop-weight tear test (DWTT) work and developed a modified back-slot (MBS) DWTT specimen to obtain higher fracture speeds. The initial experimental observations demonstrated that this type of specimen can be used to obtain higher fracture speeds. Furthermore, the experimental results clearly showed the effect of fracture speed on the ductile fracture resistance. In this paper, an in-depth study was carried out to further investigate why higher fracture speeds are obtained in the MBS-DWTT specimens. For this purpose, quasi-static and dynamic/impact DWTT experiments were conducted for both standard DWTT and MBS-DWTT specimens. In addition, finite element analyses using cohesive zone model were carried out to investigate the fracture behavior in these tests. In summary, the higher fracture speeds in the MBS-DWTT come from two major factors. First, as demonstrated by the quasi-static test results, the natural unloading characteristics of the MBS-DWTT specimen (even under pure displacement-controlled loading) leads to higher fracture speeds. Second, the steep unloading curve of the MBS-DWTT specimen produces a higher possibility of an unstable ductile fracture even during the impact event, which will result in higher fracture speeds.
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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) | 0.003 | 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".