Occurrence of Dynamic Shear Bands in AISI 4340 Steel under Impact Loads
Bibliographic record
Abstract
In this study, occurrence of adiabatic shear bands in AISI 4340 steel under high velocity impact loads is investigated using finite element analysis and experimental tests. The cylindrical steel specimen subjected to impact load was divided into different sections separated by nodes using finite element method in ABAQUS environment with boundary conditions specified. The material properties were assumed to be lower at the section where the adiabatic shear bands are expected to initialize. The finite element model was used to determine the maximum flow stress, the strain hardening, the thermal softening, and the critical strain for the formation of adiabatic shear bands. Experimental results show that deformed bands were formed at low strain rates and there was a minimum strain rate required for formation of transformed band in the alloy. The experimental results also show that cracks were initiated and propagated along transformed bands leading to fragmentation under the impact loading. The susceptibility of the adiabatic shear bands to cracking was markedly influenced by strain-rates. The simulation results obtained were compared with experimental results obtained for the AISI 4340 steel under high strain-rate loading in compression using split impact Hopkinson bars. A good agreement between the experimental and simulation results was obtained.
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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 | 0.000 |
| 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) | 0.001 | 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".