Influence of carbon on the rheology and additive manufacturability of Ti-6Al-4V powders
Bibliographic record
Abstract
• Ti6Al4V powder properties become significantly worse over 0.50 wt% C inclusion. • Agglomeration of carbon around satellite particles increases cohesion reducing flow. • Carbon addition within 0.25 wt% produces high density parts with < 0.10 % porosity. • Printed parts have similar acicular α’ martensitic microstructure with no carbides. • Grade 23 exhibits better powder-part properties after carbon addition than Grade 5. The focus of this work was to determine the effect of carbon blending on powder-part properties of titanium alloy Ti-6Al-4V. To assess this, carbon blends of both grade 5 and grade 23 from 0.1-1.0 wt% C were prepared. Part printability using laser powder bed fusion (LPBF) was assessed by measuring the segregation, flowability, rheology, and spreadability of the powder. Blend quality was assessed chemically and visually via computed tomography and scanning electron microscopy. Carbon blends above 0.25 wt% C produced significant segregation of carbon particles. Agglomerated carbon segregates acted as barriers to flow causing the reduction in dynamic flow by 40–60% compared to the virgin powders. High carbon contents also limited powder spreadability by promoting large streaks during powder spreading. Below 0.25 wt% C, the deleterious effects of segregation, flowability, and spreadability were reduced and the powder characteristics were comparable to the processable virgin powders. Printed parts exhibited very small effect of carbon blending on the density and micro-hardness of the material. The grade 23 powder is more suitable for carbon blending and offers the highest part densities and lowest variation in material hardness. This is attributed to lesser carbon agglomeration, better powder flow, and fewer interstitial elements.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".