Effects of strain and strain-rate on the formation of the shear band in metals
Bibliographic record
Abstract
An experimental study on the formation of shear bands was carried out in several metals including pure titanium (α-Ti), 304 stainless steel and aluminium (Al-7075-T6) through the compression of cylinder specimens in a Split-Hopkinson-Bar system. The shear band initiation and the distribution within the specimen were examined under different plastic strains and strain rates. The shear band forms along the maximum shear stress plane. Combining the total plastic strain and the angle between the band and the centrally symmetrical axis with the stress-strain curve, the critical strain (e c ) and stress (σ c ) for the formation of the band can be evaluated. It was found that the multiple developing shear bands were stopped in front of the grain boundary in one grain of Al-7075-T6. The present experimental results agree with Armstrong's dislocation pile-up model for the initiation for shear banding. Based on dislocation dynamics, a new constitutive equation is proposed for description of the formation of shear bands. The formation of the band is caused mainly by the strain softening from the sharp increase of mobile dislocations while the thermal softening enhances the development of the shearing process.
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 | 0.000 | 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".