Shear band characteristics in high strain rate naval applications
Bibliographic record
Abstract
Abstract This paper explores the dynamic behavior of HSLA 65 naval steels, specifically focusing on the initiation and growth of shear bands in quasi-static and dynamic compression experiments and how these bands affect stress-strain responses. The results indicate that the yield strength for this HSLA 65 increases from 541 ± 8 MPa for quasi-static (10-3 s-1) to 1081 ± 48 MPa for dynamic rates 1853 ± 31 s-1, and the hardening exponent increases from 0.376 ± 0.028 for quasi-static to 0.396 ± 0.006 for dynamic rates. Yield behavior was found to be associated with the onset of shear banding for both strain-rates, confirmed through visualization of the specimen surface using high-speed and ultra-high-speed cameras. For the quasi-static case, shear banding and yielding was observed to occur at 2.5% strain, and were observed to grow at speeds of upwards of 38 mm/s. For the dynamic experiments, the shear banding begins at approximately 1.18 ± 0.06% strain and these can grow upwards of 2122 ± 213 m/s during post-yield softening. Altogether, these measurements are some of the first of their kind in the open literature, and provide guidance on the critical time and length scales in shear banding. This information can be used in the future to design more failure-resistant steels, which has broader applications in construction, defense, and natural resource industries.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".