Effect of Fracture Speed on Ductile Fracture Resistance: Part 1—Experimental
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. Over the last few decades, it has become recognized that the drop-weight tear test (DWTT) better represents the ductile fracture resistance than the Charpy test since it utilizes a specimen that has the full thickness of the pipe and has a fracture path long enough to reach steady-state fracture resistance. However, the fracture speed in the DWTT is typically 50 to 60 feet per second (15.2 to 18.3 m/s), whereas the fracture speed in the full-scale pipe test is 300 to 1,000 fps (91.4 to 305 m/s). Recently, the authors have extended the DWTT work and developed a modified back-slot DWTT specimen to obtain higher fracture speeds. One aspect of this modified specimen was to increase the width of DWTT sample from the standard 3-inch (76.2 mm) to 5-inch (127 mm). This was done to increase the ligament length in a relatively deep back-slotted specimen to capture more steady-state data. The initial experimental results 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, to extend the work on modified back-slot DWTT specimens, the tup was instrumented to measure the load during dynamic impact. From this, the load-displacement curve, steady-state energy (or energy per area) was obtained for the modified back-slot DWTT specimens. These results were compared to those obtained from the standard 3-inch specimens. These results also clearly showed the effect of fracture speed on fracture resistance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".