Effect of stress state on adiabatic shear banding in Al2024-T351
Bibliographic record
Abstract
Cubic uniaxial compression and angled cubic biaxial shear-compression specimens are a preferable choice in the context of developing empirical data for models such as the Generalized Incremental Stress State Dependent Damage Model (GISSMO), with a strain-rate dependent extension. The angled cubic specimens introduce a multiaxial stress state of combined shear and compression. Al2024-T351 specimens have been impacted using a direct impact Hopkinson bar coupled with Digital Image Correlation (2D-DIC). 2D-DIC has been used to track the full-field strain evolution of all specimens and obtain the fracture strains for fractured specimens. The shear strain evolution related to failure along the shear planes can be acquired using 2D-DIC, and a quantitative measurement of fracture initiation is acquired from the DIC data. It is also highlighted that the transverse displacement contours can offer qualitative information about the effects of friction on proportional loading during uniaxial compression. Interrupted tests using stop rings are conducted for angled cubic specimens to identify the critical strains for the initiation of adiabatic shear bands (ASBs), which can be defined as the GISSMO instability strains. ASBs are confirmed using the optical microscope, and it is found that for a constant impact momentum, the specimens with a greater shear to compressive stress state exhibit a higher tendency to form ASBs at lower major axial strains. It is also found that ASBs are observed using the optical microscope with no loss of flow stress, and that angled specimens exhibit a lower flow stress than uniaxial compression specimens at higher strain rates. Lastly, pre-impact and non-fractured post-impact Vickers microhardness tests have been conducted, and it is found that for all specimens, the material is on average about 20% harder after impact in regions away from the ASB, and further that regardless of stress-state, the ASB is consistently about 30% harder than the pre-impact microstructure.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".