High-Temperature Impact Response of Additively Manufactured Ti–6Al–4V
Bibliographic record
Abstract
Titanium alloys, specifically Ti–6Al–4V, are widely used in the aerospace industry due to their exceptional high-temperature and strain fatigue properties, combined with a relatively low density. With the recent emergence of additive manufacturing technology, the aerospace industry is increasingly adopting this technique for fabricating Ti–6Al–4V parts, allowing for greater design freedom compared to conventional techniques. However, one of the challenges is determining its high strain rate and high-temperature properties, similar to the environment in which the parts are in service. Therefore, in this work, a compressive split-Hopkinson pressure bar (SHPB) technique equipped with an infrared radiation furnace was employed to dynamically test as-printed and heat-treated Ti–6Al–4V alloy samples, fabricated using the laser powder bed fusion (LPBF) process. The results of this work established the dependence of dynamic mechanical properties on the microstructural features of the Ti–6Al–4V alloy. Moreover, the dynamic response at elevated temperatures has been investigated, specifically at strain rates and temperature ranges of 200–2500 s −1 and 25–400 °C, respectively.
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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) | 0.002 | 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".