Modeling and Simulation of Adiabatic Shear Bands in AISI 4340 Steel Under Impact Loads
Bibliographic record
Abstract
In this study, the effects of microstructure and strain rate on the occurrence and failure of adiabatic shear bands in AISI 4340 steel under high velocity impact loads are investigated using finite element analysis and experimental tests. The shear band generated due to impact load was divided into some set of elements separated by nodes using finite element method in ABAQUS environment with initial and boundary conditions specified. The material properties were assumed to be lower at the second element set in order to initialize the adiabatic shear bands. The strain energy density for each successive node was calculated successively starting from the first element where initial boundary condition, initial strain hardening constant, and stress resistance had been specified. As the load time is increased, its corresponding effect on the localized shear deformation and width of the adiabatic shear band was also determined. The finite element model was used to determine the maximum stress, the strain hardening, the thermal softening, and the time to reach critical strain for 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 and the initial material microstructures. The numerical results obtained were compared with the 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 are also obtained.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".