Phase Transformation Behaviors and Dislocation Evolutions of an Additively Manufactured Ti-6Al-4V Alloy under Annealing Treatment
Bibliographic record
Abstract
Post annealing treatment is generally needed for additively manufactured titanium alloy to decompose metastable phases, alleviate residual stress, and improve ductility. In this work, in-situ electrical resistivity and line profile analysis of X-ray diffraction were utilized for monitoring phase transformation behaviors and dislocation evolutions of a laser powder bed fusion-built Ti-6Al-4V alloy under post annealing treatment. Besides, hardness and tensile tests were adopted for revealing the effects of phase transformation and dislocation evolutions on the mechanical properties. The results indicated that post annealing treatment decomposed martensitic α′ into lamellar α + β and eliminated dislocations efficiently. The martensite decomposition rate increased with the annealing temperature. Annealing at 700 °C for 4 h eliminated 98% of the dislocations, and further annealing has only a limited influence on the dislocation density. Annealing at 700 °C for 16 h is beneficial for achieving a high ductility of 10.3% owing to the favorable equilibrium lamellar α + β microstructure. These findings provide valuable insights for optimizing post annealing treatment procedures to enhance the mechanical properties of additively manufactured Ti-6Al-4V alloys.
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 | 0.000 |
| 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".