Effect of heat treatment on stability of adiabatic shear bands in 4340 steel
Bibliographic record
Abstract
The fingerprint of deformation in materials at large strains and at high strain rates is the formation of adiabatic shear bands. Adiabatic shear bands lead to unexpected failure of materials during service. This study investigated the possibility of eliminating adiabatic shear bands from materials subjected to severe deformation at high strain rates by post impact heat treatment. Five groups of cylindrical AISI 4340 steel samples were impacted using the Direct Impact Hopkinson Pressure Bar (DIHPB) developed at the University of Manitoba. Selected impacted samples with distinct transformed shear bands were soaked at 350⁰C to 850⁰C for periods ranging from 30 minutes to 4 hours to investigate how temperature and time affects the properties and structure of the shear bands. Annealing the shear bands at 350⁰C resulted in an increase in hardness of the shear bands and the surrounding material outside the shear bands regardless of the heat treatment before impact, amount of deformation, and the time of annealing. Significant decrease in hardness of the shear bands occurred after post impact annealing at 650⁰C for 30 minutes and 2 hours. Hardness of the shear bands reduced to the same level as that of the impacted material outside the shear bands. However, the initial path of the shear bands in the impacted steel samples could be traced through a “signature” left after the annealing. Post-impact annealing of the steel samples at 750⁰C and 850⁰C resulted in a homogenous microstructure with no trace of the shear bands. The “signatures” which were used to trace the path of the shear bands in impacted samples annealed at 650⁰C disappeared and the hardness across the samples became uniform. The observations from this study show that adiabatic shear bands in typical steel can be eliminated by annealing heat treatment. The temperature of annealing is the most critical parameter and the annealing should be performed above 650⁰C.
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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 | 0.000 |
| 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".