Microstructure and Mechanical Properties of Ti-6Al-4V Additively Manufactured by Electron Beam Melting with 3D Part Nesting and Powder Reuse Influences
Bibliographic record
Abstract
To better support the transition to more industrial uses of additive manufacturing, this study examined the use of an Arcam Q20+ industrial 3D printer for producing heavily nested Ti-6Al-4V parts with both in-specification (IS) and out of specification (OS) oxygen content in reused grade 5 powder chemistries. Both the OS and IS powder chemistries were evaluated to understand their impact on build integrity and on static and fatigue performance. The results from our evaluations showed that controlling the bed preheat temperature in the Q20+ to relatively low values (326–556 °C) was effective in limiting microstructural coarsening during the long build time and enabled adequate/balanced performance vis à vis the tensile strength and ductility. Overall, the tensile properties of the IS Ti-6Al-4V material in the as-built and machined states fully met the requirements of ASTM F2924-14. By contrast, the ductility was compromised at oxygen levels above 0.2 wt.% (OS) in Ti-6Al-4V produced by EBM. Removal of the surface layer by machining increased the consistency and performance of the IS and OS Ti-6Al-4V materials. The fatigue behaviour of the EBM Ti-6Al-4V material was in the range of properties produced by casting. Due to the strong influence of both the surface finish and oxygen content on the fatigue strength, the IS Ti-6Al-4V material exhibited the highest performance, with results that were in the range of parts that had been cast plus hot isostatically pressed.
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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".