Effects of large strain reverse loading on the strain rate dependence and dynamic strain localization of ductile metallic rods
Bibliographic record
Abstract
Abstract The dynamic necking of ductile metallic rods with large strain reverse loading history has received little attention in the published literature. A novel bespoke real time strain control setup is constructed to apply the reverse loading directly to the specimen gauge section up to a maximum strain level of ± 0.16. 304L stainless steel is used as a model material in this study. The subsequent tensile tests of the reverse loaded specimens are performed from quasi-static to high strain rates of 1000/s, using a Zwick 050 Machine, hydraulic Instron 8854, and a bespoke split Hopkinson tension bar with high speed photography equipment. The initial flow stress of the 304L rods shows similar strain rate dependence, regardless of the reverse loading history. The local strain rate during strain localization increases dramatically and eventually reaches one order of magnitude higher than the nominal strain rate. A higher strain reverse loading significantly influences the development of necking instabilities, with smaller strain to necking inception, higher local stress in the necking zone, and higher local strain rate up to failure. Instead of evaluating the impact energy absorption up to necking, an analysis of the local stress–strain relationship indicates that the reverse loaded 304L shows good impact energy absorption up to failure. This agrees with the ductile fracture surfaces of the 304L materials with reverse loading.
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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) | 0.002 | 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".