Phase transformation-induced microstructural inhomogeneity in adiabatic shear bands of a metastable beta-titanium alloy
Bibliographic record
Abstract
The growing demands for industrial applications with enhanced high strain-rate performance necessitates a deeper understanding of material behavior under dynamic loading. Adiabatic shear band (ASB) formation is a primary instability in materials subjected to high strain-rate dynamic loading. Understanding the microstructural evolution within ASBs is crucial for elucidating the mechanisms of adiabatic shear instability. In this study, advanced characterization techniques and lattice strain calculations were employed to identify the intricate atomic-level deformation mechanisms in the adiabatic shear region of a metastable β titanium alloy. This investigation revealed a phase transformation sequence of β to α'' and subsequently to α, which resulted in a heterogeneous multi-phase microstructure. Additionally, nano-twinning was observed in both α''-martensite and the HCP-α phase, significantly promoting grain refinement. Inhomogeneous strain distributions at twin and phase boundaries facilitated the initiation of micro-void and micro-cracks during dynamic deformation, ultimately resulting in adiabatic shear failure. The limited temperature rise within the ASB, supported by kinetic calculations, suggested that grain refinement occurred predominantly through dislocation migration-governed dynamic recovery mechanism. By systematically correlating the microstructural evolution with the mechanical response of the alloy, the findings provide a theoretical foundation for developing high-performance materials with improved resistance to adiabatic shear failure under high-speed dynamic loading.
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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) | 0.000 | 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".