Adiabatic Shear Localization in Metallic Materials: Review
Bibliographic record
Abstract
In advanced engineering applications, there has been an increasing demand for the service performance of materials under high-strain-rate conditions where a key phenomenon of adiabatic shear instability is inevitably involved. The presence of adiabatic shear instability is typically associated with large shear strains, high strain rates, and elevated temperatures. Significant plastic deformation that concentrates within a adiabatic shear band (ASB) often results in catastrophic failure, and it is necessary to avoid the occurrence of such a phenomenon in most areas. However, in certain areas, such as high-speed machining and self-sharpening projectile penetration, this phenomenon can be exploited. The thermal softening effect and microstructural softening effect are widely recognized as the foundational theories for the formation of ASB. Thus, elucidating various complex deformation mechanisms under thermomechanical coupling along with changes in temperatures in the shear instability process has become a focal point of research. This review highlights these two important aspects and examines the development of relevant theories and experimental results, identifying key challenges faced in this field of study. Furthermore, advancements in modern experimental characterization and computational technologies, which lead to a deeper understanding of the adiabatic shear instability phenomenon, have also been summarized.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".