On the Design of Novel Multi-failure Specimens for Ductile Failure Testing
Bibliographic record
Abstract
Abstract To quantify uncertainty in the failure response of metallic alloys, conventional experiments may not be suitable owing to the lack of significant scatter in stress states that lead to ductile tearing. In contrast, our testing experience indicates with structures containing strategically located cutouts lead to multiple failure paths and display sufficient scatter in the failure response. Accordingly, we describe the design of dog-bone shaped structures with an ensemble of cutouts, so that at least three different failure paths are observed. In conjunction with Digital Image correlation for full-field displacement measurement and numerical computations, we show how multiple failure paths are obtained when these samples are used. Tests on 2-mm thick 6061-T61 aluminum sheets were conducted using a novel guillotine-style fixture that allows the use of high-speed press. The latter allows testing at strain rates as high as 1 1/s. The primary purpose of this article is to make the case for the use of these samples that not only minimize number of tests required to garner data to calibrate stress-based damage models that capture the entire failure envelope of the material, but also display sufficient scatter that allows quantification of uncertainty in the failure response.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".