On the nature of nano twin-induced shear band formation in a dispersion strengthened copper alloy under micro-mechanical loading
Bibliographic record
Abstract
A key challenge in metallic alloys with high ductility is understanding how microstructural inhomogeneities influence shear localization, leading to localized plastic deformation and material failure. In this study, we explore the phenomenon of shear localization in coarse-grained copper, a material traditionally regarded as having low susceptibility to such behavior. Contrary to conventional understanding, our findings reveal that microstructural inhomogeneities play a pivotal role in inducing extensive strain localization during low strain rate micro-mechanical loading. The presence of fine precipitate particles leads to strain localization at small strains and low strain rates by activating shear localization driven by void formation mechanisms. Furthermore, nanoscale precipitates act as sites for dislocation pile-up within deformed structures of shear bands, leading to the formation of nano twins within these bands. Consequently, precipitate particles serve a dual role: contributing to strain localization and potential cracking, while also enhancing the alloy's strength and ductility through nano-twinning mechanisms. This investigation offers a novel perspective on the interplay between material microstructure and deformation behavior, challenging existing paradigms in materials science.
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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) | 0.001 | 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".