Bibliographic record
Abstract
Adiabatic shear bands are microstructural features that appear when metals, and some non-metals are subjected to impact loading at strain rates in excess of 103 s-1 and large strains. The formation of these bands is generally attributed to several competing mechanisms, among them is an initial strain hardening followed by adiabatic thermal softening that may lead to crack initiation within the bands. The authors have developed a model for formation of adiabatic shear bands in metallic materials as they are formed during testing using a torsional Hopkinson Bar. The model relies on a one dimensional analysis which predicts accurately the two steps of forming adiabatic shear bands in terms of strain hardening followed by thermal softening. In this current research, the model is extended to a two-dimensional analysis which would be suitable for application in either a two bar compression Split Hopkinson Bar or in a direct impact compression system developed by the author (Nabil Bassim) to produce high strain rates and large strains. The algorithm relies on applying the concept of dynamic recrystallization in order to determine the onset or initiation of the adiabatic shear bands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".