Effect of strain rate on shear properties and fracture characteristics of DP600 and AA5182-O sheet metal alloys
Bibliographic record
Abstract
Shear tests were performed at strain rates ranging from quasi-static (.01 s−1) to 600 s−1 for DP600 steel and AA5182-O sheet metal alloys at room temperature. A miniature sized shear specimen was modified and validated in this work to perform high strain rate shear testing. Digital image correlation (DIC) techniques were employed to measure the strains in the experiments, and a criterion to detect the onset of fracture based on the hardening rate of the materials is proposed. At equivalent strains greater than 20%, the DP600 and AA5182 alloys demonstrated a reduced work hardening rate at elevated strain rates. At lower strains, the DP600 shows positive rate sensitivity while the AA5182 was not sensitive to strain rate. For both alloys, the equivalent fracture strain and elongation to failure decreased with strain rate. A conversion of the shear stress to an equivalent stress using the von Mises yield criterion provided excellent agreement with the results from tensile tests at elevated strain rates. Unlike the tensile test, the shear test is not limited by the onset of necking so the equivalent stress can be determined over a larger range of strain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.002 | 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".